Methods and systems for intelligent collection and analysis of vehicle data

ABSTRACT

An apparatus, methods and systems for a monitoring system for data collection in a vehicle are disclosed. The system can include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of components of the vehicle, a data analysis circuit structured to determine a state value, wherein the data analysis circuit includes a pattern recognition circuit structured to determine the state value by analyzing a subset of the plurality of detection values and at least one external detection value using at least one of a neural net or an expert system, and an analysis response circuit structured to adjust a parameter of the vehicle in response to the state value.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of, and is a continuation of,U.S. Non-Provisional patent application Ser. No. 15/973,406, filed May7, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN AN INDUSTRIALINTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGE DATA SETS(STRF-0001-U22).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) is a bypasscontinuation-in-part of International Application Number PCT/US17/31721,filed May 9, 2017, entitled METHODS AND SYSTEMS FOR THE INDUSTRIALINTERNET OF THINGS, published on Nov. 16, 2017, as WO 2017/196821(STRF-0001-WO), which claims priority to: U.S. Provisional PatentApplication Ser. No. 62/333,589, filed May 9, 2016, entitled STRONGFORCE INDUSTRIAL IOT MATRIX (STRF-0001-P01); U.S. Provisional PatentApplication Ser. No. 62/350,672, filed Jun. 15, 2016, entitled STRATEGYFOR HIGH SAMPLING RATE DIGITAL RECORDING OF MEASUREMENT WAVEFORM DATA ASPART OF AN AUTOMATED SEQUENTIAL LIST THAT STREAMS LONG-DURATION ANDGAP-FREE WAVEFORM DATA TO STORAGE FOR MORE FLEXIBLE POST-PROCESSING(STRF-0001-P02); U.S. Provisional Patent Application Ser. No.62/412,843, filed Oct. 26, 2016, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P03); and U.S. ProvisionalPatent Application Ser. No. 62/427,141, filed Nov. 28, 2016, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P04).

U.S. Ser. No. 15/973,406 (STRF-0001-U22) also claims priority to: U.S.Provisional Patent Application Ser. No. 62/540,557, filed Aug. 2, 2017,entitled SMART HEATING SYSTEMS IN AN INDUSTRIAL INTERNET OF THINGS(STRF-0001-P05); U.S. Provisional Patent Application Ser. No.62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); and U.S. ProvisionalPatent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P07).

The present application claims the benefit of, and is a bypasscontinuation of, International Application Number PCT/US18/45036, filedAug. 2, 2018, entitled METHODS AND SYSTEMS FOR DETECTION IN ANINDUSTRIAL INTERNET OF THINGS DATA COLLECTION ENVIRONMENT WITH LARGEDATA SETS (STRF-0011-WO).

International Application Number PCT/US18/45036 (STRF-0011-WO) claimsthe benefit of, and is a continuation of, U.S. Non-Provisional patentapplication Ser. No. 15/973,406, filed May 7, 2018, entitled METHODS ANDSYSTEMS FOR DETECTION IN AN INDUSTRIAL INTERNET OF THINGS DATACOLLECTION ENVIRONMENT WITH LARGE DATA SETS (STRF-0001-U22).

International Application Number PCT/US18/45036 (STRF-0011-WO) claimspriority to: U.S. Provisional Patent Application Ser. No. 62/540,557,filed Aug. 2, 2017, entitled SMART HEATING SYSTEMS IN AN INDUSTRIALINTERNET OF THINGS (STRF-0001-P05); U.S. Provisional Patent ApplicationSer. No. 62/540,513, filed Aug. 2, 2017, entitled SYSTEMS AND METHODSFOR SMART HEATING SYSTEM THAT PRODUCES AND USES HYDROGEN FUEL(STRF-0001-P08); U.S. Provisional Patent Application Ser. No.62/562,487, filed Sep. 24, 2017, entitled METHODS AND SYSTEMS FOR THEINDUSTRIAL INTERNET OF THINGS (STRF-0001-P06); and U.S. ProvisionalPatent Application Ser. No. 62/583,487, filed Nov. 8, 2017, entitledMETHODS AND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS(STRF-0001-P07).

The present application claims priority to U.S. Provisional PatentApplication Ser. No. 62/583,487, filed Nov. 8, 2017, entitled METHODSAND SYSTEMS FOR THE INDUSTRIAL INTERNET OF THINGS (STRF-0001-P07).

All of the foregoing applications are hereby incorporated by referenceas if fully set forth herein in their entirety.

BACKGROUND 1. Field

The present disclosure relates to methods and systems for datacollection in industrial environments, as well as methods and systemsfor leveraging collected data for monitoring, remote control, autonomousaction, and other activities in industrial environments.

2. Description of the Related Art

Heavy industrial environments, such as environments for large scalemanufacturing (such as manufacturing of aircraft, ships, trucks,automobiles, and large industrial machines), energy productionenvironments (such as oil and gas plants, renewable energy environments,and others), energy extraction environments (such as mining, drilling,and the like), construction environments (such as for construction oflarge buildings), and others, involve highly complex machines, devicesand systems and highly complex workflows, in which operators mustaccount for a host of parameters, metrics, and the like in order tooptimize design, development, deployment, and operation of differenttechnologies in order to improve overall results. Historically, data hasbeen collected in heavy industrial environments by human beings usingdedicated data collectors, often recording batches of specific sensordata on media, such as tape or a hard drive, for later analysis. Batchesof data have historically been returned to a central office foranalysis, such as undertaking signal processing or other analysis on thedata collected by various sensors, after which analysis can be used as abasis for diagnosing problems in an environment and/or suggesting waysto improve operations. This work has historically taken place on a timescale of weeks or months, and has been directed to limited data sets.

The emergence of the Internet of Things (IoT) has made it possible toconnect continuously to, and among, a much wider range of devices. Mostsuch devices are consumer devices, such as lights, thermostats, and thelike. More complex industrial environments remain more difficult, as therange of available data is often limited, and the complexity of dealingwith data from multiple sensors makes it much more difficult to produce“smart” solutions that are effective for the industrial sector. A needexists for improved methods and systems for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control,intelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments.

Industrial system in various environments have a number of challenges toutilizing data from a multiplicity of sensors. Many industrial systemshave a wide range of computing resources and network capabilities at alocation at a given time, for example as parts of the system areupgraded or replaced on varying time scales, as mobile equipment entersor leaves a location, and due to the capital costs and risks ofupgrading equipment. Additionally, many industrial systems arepositioned in challenging environments, where network connectivity canbe variable, where a number of noise sources such as vibrational noiseand electro-magnetic (EM) noise sources can be significant an in variedlocations, and with portions of the system having high pressure, highnoise, high temperature, and corrosive materials. Many industrialprocesses are subject to high variability in process operatingparameters and non-linear responses to off-nominal operations.Accordingly, sensing requirements for industrial processes can vary withtime, operating stages of a process, age and degradation of equipment,and operating conditions. Previously known industrial processes sufferfrom sensing configurations that are conservative, detecting manyparameters that are not needed during most operations of the industrialsystem, or that accept risk in the process, and do not detect parametersthat are only occasionally utilized in characterizing the system.Further, previously known industrial systems are not flexible toconfiguring sensed parameters rapidly and in real-time, and in managingsystem variance such as intermittent network availability. Industrialsystems often use similar components across systems such as pumps,mixers, tanks, and fans. However, previously known industrial systems donot have a mechanism to leverage data from similar components that maybe used in a different type of process, and/or that may be unavailabledue to competitive concerns. Additionally, previously known industrialsystems do not integrate data from offset systems into the sensor planand execution in real time.

SUMMARY

The present disclosure describes a system for data collection in avehicle, the system, according to one disclosed non-limiting embodimentof the present disclosure, can include a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to input received from atleast one of a plurality of input sensors, each of the plurality ofinput sensors operatively coupled to at least one of a plurality ofcomponents of the vehicle, a data analysis circuit structured todetermine a state value, wherein the data analysis circuit includes apattern recognition circuit structured to determine the state value byanalyzing a subset of the plurality of detection values and at least oneexternal detection value using at least one of a neural net or an expertsystem, and an analysis response circuit structured to adjust aparameter of the vehicle in response to the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the analysis response circuitis further structured to adjust a detection package in response to thestate value, wherein the detection package includes a selection ofavailable sensors utilized as the plurality of input sensors.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the detection package furtherincludes a sensor parameter for at least one of the plurality of inputsensors.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the state value includes atleast one of: an off-nominal operation, a component failure, a componentfault, and a component maintenance requirement and wherein the adjustingthe detection package includes enhancing a resolution of the detectionvalues in response to the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein enhancing the resolution ofthe detection values includes at least one of enhancing a sensorresolution, changing from a first input sensor to a second input sensorhaving a higher resolution capability than the first input sensor, andchanging a data storage profile to enhance a resolution of stored dataof the detection values.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system performs a pattern recognition operation todetermine the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the pattern recognitionoperation is performed on vibration data of the plurality of detectionvalues.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system further compares the vibration data of theplurality of detection values to a library of noise patterns, whereinthe library of noise patterns includes the at least one external datavalue.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system is configured to at least intermittently accessa self-organizing marketplace, and wherein the self-organizingmarketplace provides the library of noise patterns.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system is configured to provide at least a portion ofthe vibration data to the self-organizing marketplace.

The present disclosure describes a method, according to one disclosednon-limiting embodiment of the present disclosure, the method caninclude interpreting a plurality of detection values of a vehicle, eachof the plurality of detection values corresponding to input receivedfrom at least one of a plurality of input sensors, each of the pluralityof input sensors operatively coupled to at least one component of thevehicle, operating at least one of a neural net or an expert system onthe plurality of detection values to determine a state value for atleast one of a component or the vehicle and adjusting at least one of asensing parameter or a data storage profile in response to the statevalue.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the state value includes atleast one of: an off-nominal operation, a component failure, a componentfault, and a component maintenance requirement, and wherein theadjusting the at least one of the sensing parameter or the data storageprofile includes enhancing a resolution of the detection values inresponse to the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system performs a pattern recognition operation todetermine the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one of the neuralnet or the expert system accesses external data value from aself-organizing marketplace, and further determines the state value inresponse to the external data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the external data valueincludes a library of noise patterns.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the library of noise patternsincludes a vibration fingerprint for a component of the vehicle.

The present disclosure describes an apparatus, according to onedisclosed non-limiting embodiment of the present disclosure, theapparatus can include a data acquisition circuit structured to interpreta plurality of detection values, each of the plurality of detectionvalues corresponding to input received from at least one of a pluralityof input sensors, each of the plurality of input sensors operativelycoupled to at least one of a plurality of components of a vehicle, adata analysis circuit structured to determine a state value, wherein thedata analysis circuit includes a pattern recognition circuit structuredto determine the state value by performing a pattern recognitionoperation on a subset of the plurality of detection values and at leastone external detection value using at least one of a neural net or anexpert system and an analysis response circuit structured to adjust aparameter of the vehicle in response to the state value.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the adjusting the parameter ofthe vehicle includes adjusting operations of the vehicle to reduce awork load on a component of the vehicle.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the state value includes anormal operating state for a component of the vehicle, and wherein theadjusting the parameter of the vehicle includes reducing an amount ofdata of the plurality of detection values that is stored relating to thecomponent of the vehicle.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the state value includes, fora component of the vehicle, at least one of: an off-nominal operation, afailure, a fault, or a maintenance requirement, and wherein theadjusting the parameter of the vehicle includes increasing an amount ofdata of the plurality of detection values that is stored relating to thecomponent of the vehicle.

Methods and systems are provided herein for data collection inindustrial environments, as well as for improved methods and systems forusing collected data to provide improved monitoring, control, andintelligent diagnosis of problems and intelligent optimization ofoperations in various heavy industrial environments. These methods andsystems include methods, systems, components, devices, workflows,services, processes, and the like that are deployed in variousconfigurations and locations, such as: (a) at the “edge” of the Internetof Things, such as in the local environment of a heavy industrialmachine; (b) in data transport networks that move data between localenvironments of heavy industrial machines and other environments, suchas of other machines or of remote controllers, such as enterprises thatown or operate the machines or the facilities in which the machines areoperated; and (c) in locations where facilities are deployed to controlmachines or their environments, such as cloud-computing environments andon-premises computing environments of enterprises that own or controlheavy industrial environments or the machines, devices or systemsdeployed in them. These methods and systems include a range of ways forproviding improved data include a range of methods and systems forproviding improved data collection, as well as methods and systems fordeploying increased intelligence at the edge, in the network, and in thecloud or premises of the controller of an industrial environment.

Methods and systems are disclosed herein for continuous ultrasonicmonitoring, including providing continuous ultrasonic monitoring ofrotating elements and bearings of an energy production facility; forcloud-based systems including machine pattern recognition based on thefusion of remote, analog industrial sensors or machine pattern analysisof state information from multiple analog industrial sensors to provideanticipated state information for an industrial system; for on-devicesensor fusion and data storage for industrial IoT devices, includingon-device sensor fusion and data storage for an Industrial IoT device,where data from multiple sensors are multiplexed at the device forstorage of a fused data stream; and for self-organizing systemsincluding a self-organizing data marketplace for industrial IoT data,including a self-organizing data marketplace for industrial IoT data,where available data elements are organized in the marketplace forconsumption by consumers based on training a self-organizing facilitywith a training set and feedback from measures of marketplace success,for self-organizing data pools, including self-organization of datapools based on utilization and/or yield metrics, including utilizationand/or yield metrics that are tracked for a plurality of data pools, aself-organized swarm of industrial data collectors, including aself-organizing swarm of industrial data collectors that organize amongthemselves to optimize data collection based on the capabilities andconditions of the members of the swarm, a self-organizing collector,including a self-organizing, multi-sensor data collector that canoptimize data collection, power and/or yield based on conditions in itsenvironment, a self-organizing storage for a multi-sensor datacollector, including self-organizing storage for a multi-sensor datacollector for industrial sensor data, a self-organizing network codingfor a multi-sensor data network, including self-organizing networkcoding for a data network that transports data from multiple sensors inan industrial data collection environment.

Methods and systems are disclosed herein for training artificialintelligence (“AI”) models based on industry-specific feedback,including training an AI model based on industry-specific feedback thatreflects a measure of utilization, yield, or impact, where the AI modeloperates on sensor data from an industrial environment; for anindustrial IoT distributed ledger, including a distributed ledgersupporting the tracking of transactions executed in an automated datamarketplace for industrial IoT data; for a network-sensitive collector,including a network condition-sensitive, self-organizing, multi-sensordata collector that can optimize based on bandwidth, quality of service,pricing, and/or other network conditions; for a remotely organizeduniversal data collector that can power up and down sensor interfacesbased on need and/or conditions identified in an industrial datacollection environment; and for a haptic or multi-sensory userinterface, including a wearable haptic or multi-sensory user interfacefor an industrial sensor data collector, with vibration, heat,electrical, and/or sound outputs.

Methods and systems are disclosed herein for a presentation layer foraugmented reality and virtual reality (AR/VR) industrial glasses, whereheat map elements are presented based on patterns and/or parameters incollected data; and for condition-sensitive, self-organized tuning ofAR/VR interfaces based on feedback metrics and/or training in industrialenvironments.

In embodiments, a system for data collection, processing, andutilization of signals from at least a first element in a first machinein an industrial environment includes a platform including a computingenvironment connected to a local data collection system having at leasta first sensor signal and a second sensor signal obtained from at leastthe first machine in the industrial environment. The system includes afirst sensor in the local data collection system configured to beconnected to the first machine and a second sensor in the local datacollection system. The system further includes a crosspoint switch inthe local data collection system having multiple inputs and multipleoutputs including a first input connected to the first sensor and asecond input connected to the second sensor. Throughout the presentdisclosure, wherever a crosspoint switch, multiplexer (MUX) device, orother multiple-input multiple-output data collection or communicationdevice is described, any multi-sensor acquisition device is alsocontemplated herein. In certain embodiments, a multi-sensor acquisitiondevice includes one or more channels configured for, or compatible with,an analog sensor input. The multiple outputs include a first output andsecond output configured to be switchable between a condition in whichthe first output is configured to switch between delivery of the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from the second output. Each ofmultiple inputs is configured to be individually assigned to any of themultiple outputs, or combined in any subsets of the inputs to theoutputs. Unassigned outputs are configured to be switched off, forexample by producing a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data about the industrial environment. Inembodiments, the second sensor in the local data collection system isconfigured to be connected to the first machine. In embodiments, thesecond sensor in the local data collection system is configured to beconnected to a second machine in the industrial environment. Inembodiments, the computing environment of the platform is configured tocompare relative phases of the first and second sensor signals. Inembodiments, the first sensor is a single-axis sensor and the secondsensor is a three-axis sensor. In embodiments, at least one of themultiple inputs of the crosspoint switch includes internet protocol,front-end signal conditioning, for improved signal-to-noise ratio. Inembodiments, the crosspoint switch includes a third input that isconfigured with a continuously monitored alarm having a pre-determinedtrigger condition when the third input is unassigned to or undetected atany of the multiple outputs.

In embodiments, the local data collection system includes multiplemultiplexing units and multiple data acquisition units receivingmultiple data streams from multiple machines in the industrialenvironment. In embodiments, the local data collection system includesdistributed complex programmable logic device (“CPLD”) chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system is configured toprovide high-amperage input capability using solid state relays. Inembodiments, the local data collection system is configured topower-down at least one of an analog sensor channel and a componentboard.

In embodiments, the local data collection system includes a phase-lockloop band-pass tracking filter configured to obtain slow-speedrevolutions per minute (“RPMs”) and phase information. In embodiments,the local data collection system is configured to digitally derive phaseusing on-board timers relative to at least one trigger channel and atleast one of the multiple inputs. In embodiments, the local datacollection system includes a peak-detector configured to autoscale usinga separate analog-to-digital converter for peak detection. Inembodiments, the local data collection system is configured to route atleast one trigger channel that is raw and buffered into at least one ofthe multiple inputs. In embodiments, the local data collection systemincludes at least one delta-sigma analog-to-digital converter that isconfigured to increase input oversampling rates to reduce sampling rateoutputs and to minimize anti-aliasing filter requirements. Inembodiments, the distributed CPLD chips each dedicated to the data busfor logic control of the multiple multiplexing units and the multipledata acquisition units includes as high-frequency crystal clockreference configured to be divided by at least one of the distributedCPLD chips for at least one delta-sigma analog-to-digital converter toachieve lower sampling rates without digital resampling.

In embodiments, the local data collection system is configured to obtainlong blocks of data at a single relatively high-sampling rate as opposedto multiple sets of data taken at different sampling rates. Inembodiments, the single relatively high-sampling rate corresponds to amaximum frequency of about forty kilohertz. In embodiments, the longblocks of data are for a duration that is in excess of one minute. Inembodiments, the local data collection system includes multiple dataacquisition units each having an onboard card set configured to storecalibration information and maintenance history of a data acquisitionunit in which the onboard card set is located. In embodiments, the localdata collection system is configured to plan data acquisition routesbased on hierarchical templates.

In embodiments, the local data collection system is configured to managedata collection bands. In embodiments, the data collection bands definea specific frequency band and at least one of a group of spectral peaks,a true-peak level, a crest factor derived from a time waveform, and anoverall waveform derived from a vibration envelope. In embodiments, thelocal data collection system includes a neural net expert system usingintelligent management of the data collection bands. In embodiments, thelocal data collection system is configured to create data acquisitionroutes based on hierarchical templates that each include the datacollection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the local data collection system includes a graphicaluser interface (“GUI”) system configured to manage the data collectionbands. In embodiments, the GUI system includes an expert systemdiagnostic tool. In embodiments, the platform includes cloud-based,machine pattern analysis of state information from multiple sensors toprovide anticipated state information for the industrial environment. Inembodiments, the platform is configured to provide self-organization ofdata pools based on at least one of the utilization metrics and yieldmetrics. In embodiments, the platform includes a self-organized swarm ofindustrial data collectors. In embodiments, the local data collectionsystem includes a wearable haptic user interface for an industrialsensor data collector with at least one of vibration, heat, electrical,and sound outputs.

In embodiments, multiple inputs of the crosspoint switch include a thirdinput connected to the second sensor and a fourth input connected to thesecond sensor. The first sensor signal is from a single-axis sensor atan unchanging location associated with the first machine. Inembodiments, the second sensor is a three-axis sensor. In embodiments,the local data collection system is configured to record gap-freedigital waveform data simultaneously from at least the first input, thesecond input, the third input, and the fourth input. In embodiments, theplatform is configured to determine a change in relative phase based onthe simultaneously recorded gap-free digital waveform data. Inembodiments, the second sensor is configured to be movable to aplurality of positions associated with the first machine while obtainingthe simultaneously recorded gap-free digital waveform data. Inembodiments, multiple outputs of the crosspoint switch include a thirdoutput and fourth output. The second, third, and fourth outputs areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, theplatform is configured to determine an operating deflection shape basedon the change in relative phase and the simultaneously recorded gap-freedigital waveform data.

In embodiments, the unchanging location is a position associated withthe rotating shaft of the first machine. In embodiments, tri-axialsensors in the sequence of the tri-axial sensors are each located atdifferent positions on the first machine but are each associated withdifferent bearings in the machine. In embodiments, tri-axial sensors inthe sequence of the tri-axial sensors are each located at similarpositions associated with similar bearings but are each associated withdifferent machines. In embodiments, the local data collection system isconfigured to obtain the simultaneously recorded gap-free digitalwaveform data from the first machine while the first machine and asecond machine are both in operation. In embodiments, the local datacollection system is configured to characterize a contribution from thefirst machine and the second machine in the simultaneously recordedgap-free digital waveform data from the first machine. In embodiments,the simultaneously recorded gap-free digital waveform data has aduration that is in excess of one minute.

In embodiments, a method of monitoring a machine having at least oneshaft supported by a set of bearings includes monitoring a first datachannel assigned to a single-axis sensor at an unchanging locationassociated with the machine. The method includes monitoring second,third, and fourth data channels each assigned to an axis of a three-axissensor. The method includes recording gap-free digital waveform datasimultaneously from all of the data channels while the machine is inoperation and determining a change in relative phase based on thedigital waveform data.

In embodiments, the tri-axial sensor is located at a plurality ofpositions associated with the machine while obtaining the digitalwaveform. In embodiments, the second, third, and fourth channels areassigned together to a sequence of tri-axial sensors each located atdifferent positions associated with the machine. In embodiments, thedata is received from all of the sensors simultaneously. In embodiments,the method includes determining an operating deflection shape based onthe change in relative phase information and the waveform data. Inembodiments, the unchanging location is a position associated with theshaft of the machine. In embodiments, the tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings in themachine. In embodiments, the unchanging location is a positionassociated with the shaft of the machine. The tri-axial sensors in thesequence of the tri-axial sensors are each located at differentpositions and are each associated with different bearings that supportthe shaft in the machine.

In embodiments, the method includes monitoring the first data channelassigned to the single-axis sensor at an unchanging location located ona second machine. The method includes monitoring the second, the third,and the fourth data channels, each assigned to the axis of a three-axissensor that is located at the position associated with the secondmachine. The method also includes recording gap-free digital waveformdata simultaneously from all of the data channels from the secondmachine while both of the machines are in operation. In embodiments, themethod includes characterizing the contribution from each of themachines in the gap-free digital waveform data simultaneously from thesecond machine.

In embodiments, a method for data collection, processing, andutilization of signals with a platform monitoring at least a firstelement in a first machine in an industrial environment includesobtaining, automatically with a computing environment, at least a firstsensor signal and a second sensor signal with a local data collectionsystem that monitors at least the first machine. The method includesconnecting a first input of a crosspoint switch of the local datacollection system to a first sensor and a second input of the crosspointswitch to a second sensor in the local data collection system. Themethod includes switching between a condition in which a first output ofthe crosspoint switch alternates between delivery of at least the firstsensor signal and the second sensor signal and a condition in whichthere is simultaneous delivery of the first sensor signal from the firstoutput and the second sensor signal from a second output of thecrosspoint switch. The method also includes switching off unassignedoutputs of the crosspoint switch into a high-impedance state.

In embodiments, the first sensor signal and the second sensor signal arecontinuous vibration data from the industrial environment. Inembodiments, the second sensor in the local data collection system isconnected to the first machine. In embodiments, the second sensor in thelocal data collection system is connected to a second machine in theindustrial environment. In embodiments, the method includes comparing,automatically with the computing environment, relative phases of thefirst and second sensor signals. In embodiments, the first sensor is asingle-axis sensor and the second sensor is a three-axis sensor. Inembodiments, at least the first input of the crosspoint switch includesinternet protocol front-end signal conditioning for improvedsignal-to-noise ratio.

In embodiments, the method includes continuously monitoring at least athird input of the crosspoint switch with an alarm having apre-determined trigger condition when the third input is unassigned toany of multiple outputs on the crosspoint switch. In embodiments, thelocal data collection system includes multiple multiplexing units andmultiple data acquisition units receiving multiple data streams frommultiple machines in the industrial environment. In embodiments, thelocal data collection system includes distributed CPLD chips eachdedicated to a data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units that receive the multipledata streams from the multiple machines in the industrial environment.In embodiments, the local data collection system provides high-amperageinput capability using solid state relays.

In embodiments, the method includes powering down at least one of ananalog sensor channel and a component board of the local data collectionsystem. In embodiments, the local data collection system includes anexternal voltage reference for an A/D zero reference that is independentof the voltage of the first sensor and the second sensor. Inembodiments, the local data collection system includes a phase-lock loopband-pass tracking filter that obtains slow-speed RPMs and phaseinformation. In embodiments, the method includes digitally derivingphase using on-board timers relative to at least one trigger channel andat least one of multiple inputs on the crosspoint switch.

In embodiments, the method includes auto-scaling with a peak-detectorusing a separate analog-to-digital converter for peak detection. Inembodiments, the method includes routing at least one trigger channelthat is raw and buffered into at least one of multiple inputs on thecrosspoint switch. In embodiments, the method includes increasing inputoversampling rates with at least one delta-sigma analog-to-digitalconverter to reduce sampling rate outputs and to minimize anti-aliasingfilter requirements. In embodiments, the distributed CPLD chips are eachdedicated to the data bus for logic control of the multiple multiplexingunits and the multiple data acquisition units and each include ahigh-frequency crystal clock reference divided by at least one of thedistributed CPLD chips for at least one delta-sigma analog-to-digitalconverter to achieve lower sampling rates without digital resampling. Inembodiments, the method includes obtaining long blocks of data at asingle relatively high-sampling rate with the local data collectionsystem as opposed to multiple sets of data taken at different samplingrates. In embodiments, the single relatively high-sampling ratecorresponds to a maximum frequency of about forty kilohertz. Inembodiments, the long blocks of data are for a duration that is inexcess of one minute. In embodiments, the local data collection systemincludes multiple data acquisition units and each data acquisition unithas an onboard card set that stores calibration information andmaintenance history of a data acquisition unit in which the onboard cardset is located.

In embodiments, the method includes planning data acquisition routesbased on hierarchical templates associated with at least the firstelement in the first machine in the industrial environment. Inembodiments, the local data collection system manages data collectionbands that define a specific frequency band and at least one of a groupof spectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope. Inembodiments, the local data collection system includes a neural netexpert system using intelligent management of the data collection bands.In embodiments, the local data collection system creates dataacquisition routes based on hierarchical templates that each include thedata collection bands related to machines associated with the dataacquisition routes. In embodiments, at least one of the hierarchicaltemplates is associated with multiple interconnected elements of thefirst machine. In embodiments, at least one of the hierarchicaltemplates is associated with similar elements associated with at leastthe first machine and a second machine. In embodiments, at least one ofthe hierarchical templates is associated with at least the first machinebeing proximate in location to a second machine.

In embodiments, the method includes controlling a GUI system of thelocal data collection system to manage the data collection bands. TheGUI system includes an expert system diagnostic tool. In embodiments,the computing environment of the platform includes cloud-based, machinepattern analysis of state information from multiple sensors to provideanticipated state information for the industrial environment. Inembodiments, the computing environment of the platform providesself-organization of data pools based on at least one of the utilizationmetrics and yield metrics. In embodiments, the computing environment ofthe platform includes a self-organized swarm of industrial datacollectors. In embodiments, each of multiple inputs of the crosspointswitch is individually assignable to any of multiple outputs of thecrosspoint switch.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for capturing a plurality ofstreams of sensed data from sensors deployed to monitor aspects of anindustrial machine associated with at least one moving part of themachine; at least one of the streams contains a plurality of frequenciesof data. The method may include identifying a subset of data in at leastone of the plurality of streams that corresponds to data representing atleast one predefined frequency. The at least one predefined frequency isrepresented by a set of data collected from alternate sensors deployedto monitor aspects of the industrial machine associated with the atleast one moving part of the machine. The method may further includeprocessing the identified data with a data processing facility thatprocesses the identified data with an algorithm configured to be appliedto the set of data collected from alternate sensors. Lastly, the methodmay include storing the at least one of the streams of data, theidentified subset of data, and a result of processing the identifieddata in an electronic data set.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing, andstorage systems and may include a method for applying data captured fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. The data is captured withpredefined lines of resolution covering a predefined frequency range andis sent to a frequency matching facility that identifies a subset ofdata streamed from other sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine. The streamed data includes a plurality of lines of resolutionand frequency ranges. The subset of data identified corresponds to thelines of resolution and predefined frequency range. This method mayinclude storing the subset of data in an electronic data record in aformat that corresponds to a format of the data captured with predefinedlines of resolution and signaling to a data processing facility thepresence of the stored subset of data. This method may, optionally,include processing the subset of data with at least one set ofalgorithms, models and pattern recognizers that corresponds toalgorithms, models and pattern recognizers associated with processingthe data captured with predefined lines of resolution covering apredefined frequency range.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for identifying a subset ofstreamed sensor data, the sensor data captured from sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the subset of streamed sensor data atpredefined lines of resolution for a predefined frequency range, andestablishing a first logical route for communicating electronicallybetween a first computing facility performing the identifying and asecond computing facility, wherein identified subset of the streamedsensor data is communicated exclusively over the established firstlogical route when communicating the subset of streamed sensor data fromthe first facility to the second facility. This method may furtherinclude establishing a second logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat is not the identified subset. Additionally, this method may furtherinclude establishing a third logical route for communicatingelectronically between the first computing facility and the secondcomputing facility for at least one portion of the streamed sensor datathat includes the identified subset and at least one other portion ofthe data not represented by the identified subset.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a first data sensing and processingsystem that captures first data from a first set of sensors deployed tomonitor aspects of an industrial machine associated with at least onemoving part of the machine, the first data covering a set of lines ofresolution and a frequency range. This system may include a second datasensing and processing system that captures and streams a second set ofdata from a second set of sensors deployed to monitor aspects of theindustrial machine associated with at least one moving part of themachine, the second data covering a plurality of lines of resolutionthat includes the set of lines of resolution and a plurality offrequencies that includes the frequency range. The system may enableselecting a portion of the second data that corresponds to the set oflines of resolution and the frequency range of the first data, andprocessing the selected portion of the second data with the first datasensing and processing system.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for automatically processing aportion of a stream of sensed data. The sensed data is received from afirst set of sensors deployed to monitor aspects of an industrialmachine associated with at least one moving part of the machine. Thesensed data is in response to an electronic data structure thatfacilitates extracting a subset of the stream of sensed data thatcorresponds to a set of sensed data received from a second set ofsensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The set ofsensed data is constrained to a frequency range. The stream of senseddata includes a range of frequencies that exceeds the frequency range ofthe set of sensed data, the processing comprising executing an algorithmon a portion of the stream of sensed data that is constrained to thefrequency range of the set of sensed data, the algorithm configured toprocess the set of sensed data.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate and integrate with existing data collection, processing andstorage systems and may include a method for receiving first data fromsensors deployed to monitor aspects of an industrial machine associatedwith at least one moving part of the machine. This method may furtherinclude detecting at least one of a frequency range and lines ofresolution represented by the first data; receiving a stream of datafrom sensors deployed to monitor the aspects of the industrial machineassociated with the at least one moving part of the machine. The streamof data includes: (1) a plurality of frequency ranges and a plurality oflines of resolution that exceeds the frequency range and the lines ofresolution represented by the first data; (2) a set of data extractedfrom the stream of data that corresponds to at least one of thefrequency range and the lines of resolution represented by the firstdata; and (3) the extracted set of data which is processed with a dataprocessing algorithm that is configured to process data within thefrequency range and within the lines of resolution of the first data.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 through FIG. 5 are diagrammatic views that each depicts portionsof an overall view of an industrial Internet of Things (IoT) datacollection, monitoring and control system in accordance with the presentdisclosure.

FIG. 6 is a diagrammatic view of a platform including a local datacollection system disposed in an industrial environment for collectingdata from or about the elements of the environment, such as machines,components, systems, sub-systems, ambient conditions, states, workflows,processes, and other elements in accordance with the present disclosure.

FIG. 7 is a diagrammatic view that depicts elements of an industrialdata collection system for collecting analog sensor data in anindustrial environment in accordance with the present disclosure.

FIG. 8 is a diagrammatic view of a rotating or oscillating machinehaving a data acquisition module that is configured to collect waveformdata in accordance with the present disclosure.

FIG. 9 is a diagrammatic view of an exemplary tri-axial sensor mountedto a motor bearing of an exemplary rotating machine in accordance withthe present disclosure.

FIG. 10 and FIG. 11 are diagrammatic views of an exemplary tri-axialsensor and a single-axis sensor mounted to an exemplary rotating machinein accordance with the present disclosure.

FIG. 12 is a diagrammatic view of multiple machines under survey withensembles of sensors in accordance with the present disclosure.

FIG. 13 is a diagrammatic view of hybrid relational metadata and abinary storage approach in accordance with the present disclosure.

FIG. 14 through FIG. 18 are diagrammatic view of components andinteractions of a data collection architecture involving data channelmethods and systems for data collection of industrial machines inaccordance with the present disclosure.

FIG. 19 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 20 and FIG. 21 are diagrammatic views that depict embodiments of adata monitoring device in accordance with the present disclosure.

FIG. 22 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 23 and 24 are diagrammatic views that depict an embodiment of asystem for data collection in accordance with the present disclosure.

FIGS. 25 and 26 are diagrammatic views that depict an embodiment of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 27 depicts an embodiment of a data monitoring device incorporatingsensors in accordance with the present disclosure.

FIGS. 28 and 29 are diagrammatic views that depict embodiments of a datamonitoring device in communication with external sensors in accordancewith the present disclosure.

FIG. 30 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 31 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 32 is a diagrammatic view that depicts embodiments of a datamonitoring device with additional detail in the signal evaluationcircuit in accordance with the present disclosure.

FIG. 33 is a diagrammatic view that depicts embodiments of a system fordata collection in accordance with the present disclosure.

FIG. 34 is a diagrammatic view that depicts embodiments of a system fordata collection comprising a plurality of data monitoring devices inaccordance with the present disclosure.

FIG. 35 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 36 and 37 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 38 and 39 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 40 and 41 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 42 and 43 are diagrammatic views that depicts embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 44 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 45 and 46 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 47 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 48 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 49 and 50 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 51 and 52 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 53 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 54 and 55 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 56 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 57 and 58 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 59 and 60 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 61 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 62 and 63 are diagrammatic views that depict embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 64 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIGS. 65 and 66 are diagrammatic views that depict embodiments of asystem for data collection in accordance with the present disclosure.

FIGS. 67 and 68 are diagrammatic views that depict embodiments of asystem for data collection comprising a plurality of data monitoringdevices in accordance with the present disclosure.

FIG. 69 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 70 is a diagrammatic view that depicts embodiments of a datamonitoring device in accordance with the present disclosure.

FIG. 71 is a diagrammatic view that depicts a monitoring system thatemploys data collection bands in accordance with the present disclosure.

FIG. 72 is a diagrammatic view that depicts a system that employsvibration and other noise in predicting states and outcomes inaccordance with the present disclosure.

FIG. 73 is a schematic flow diagram of a procedure for data collectionin an industrial environment in accordance with the present disclosure.

FIG. 74 is a diagrammatic view that depicts industry-specific feedbackin an industrial environment in accordance with the present disclosure.

FIG. 75 is a diagrammatic view that depicts an exemplary user interfacefor smart band configuration of a system for data collection in anindustrial environment is depicted in accordance with the presentdisclosure.

FIG. 76 is a diagrammatic view that depicts a graphical approach 11300for back-calculation in accordance with the present disclosure.

FIG. 77 is a diagrammatic view that depicts a wearable haptic userinterface device for providing haptic stimuli to a user that isresponsive to data collected in an industrial environment by a systemadapted to collect data in the industrial environment in accordance withthe present disclosure.

FIG. 78 is a diagrammatic view that depicts an augmented reality displayof heat maps based on data collected in an industrial environment by asystem adapted to collect data in the environment in accordance with thepresent disclosure.

FIG. 79 is a diagrammatic view that depicts an augmented reality displayincluding real time data overlaying a view of an industrial environmentin accordance with the present disclosure.

FIG. 80 is a diagrammatic view that depicts data collection systemaccording to some aspects of the present disclosure.

FIG. 81 is a diagrammatic view that depicts embodiments of a storagetime definition in accordance with the present disclosure.

FIG. 82 is a diagrammatic view that depicts embodiments of a dataresolution description in accordance with the present disclosure.

FIG. 83 is a diagrammatic view of an apparatus for self-organizingnetwork coding for data collection for an industrial system inaccordance with the present disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

Methods and systems described herein for industrial machine sensor datastreaming, collection, processing, and storage may be configured tooperate with existing data collection, processing, and storage systemswhile preserving access to existing format/frequency range/resolutioncompatible data. While the industrial machine sensor data streamingfacilities described herein may collect a greater volume of data (e.g.,longer duration of data collection) from sensors at a wider range offrequencies and with greater resolution than existing data collectionsystems, methods and systems may be employed to provide access to datafrom the stream of data that represents one or more ranges of frequencyand/or one or more lines of resolution that are purposely compatiblewith existing systems. Further, a portion of the streamed data may beidentified, extracted, stored, and/or forwarded to existing dataprocessing systems to facilitate operation of existing data processingsystems that substantively matches operation of existing data processingsystems using existing collection-based data. In this way, a newlydeployed system for sensing aspects of industrial machines, such asaspects of moving parts of industrial machines, may facilitate continueduse of existing sensed data processing facilities, algorithms, models,pattern recognizers, user interfaces, and the like.

Through identification of existing frequency ranges, formats, and/orresolution, such as by accessing a data structure that defines theseaspects of existing data, higher resolution streamed data may beconfigured to represent a specific frequency, frequency range, format,and/or resolution. This configured streamed data can be stored in a datastructure that is compatible with existing sensed data structures sothat existing processing systems and facilities can access and processthe data substantially as if it were the existing data. One approach toadapting streamed data for compatibility with existing sensed data mayinclude aligning the streamed data with existing data so that portionsof the streamed data that align with the existing data can be extracted,stored, and made available for processing with existing data processingmethods. Alternatively, data processing methods may be configured toprocess portions of the streamed data that correspond, such as throughalignment, to the existing data, with methods that implement functionssubstantially similar to the methods used to process existing data, suchas methods that process data that contain a particular frequency rangeor a particular resolution and the like.

Methods used to process existing data may be associated with certaincharacteristics of sensed data, such as certain frequency ranges,sources of data, and the like. As an example, methods for processingbearing sensing information for a moving part of an industrial machinemay be capable of processing data from bearing sensors that fall into aparticular frequency range. This method can thusly be at least partiallyidentifiable by these characteristics of the data being processed.Therefore, given a set of conditions, such as moving device beingsensed, industrial machine type, frequency of data being sensed, and thelike, a data processing system may select an appropriate method. Also,given such a set of conditions, an industrial machine data sensing andprocessing facility may configure elements, such as data filters,routers, processors, and the like, to handle data meeting theconditions.

FIGS. 1 through 5 depict portions of an overall view of an industrialInternet of Things (IoT) data collection, monitoring and control system10. FIG. 2 depicts a mobile ad hoc network (“MANET”) 20, which may forma secure, temporal network connection 22 (sometimes connected andsometimes isolated), with a cloud 30 or other remote networking system,so that network functions may occur over the MANET 20 within theenvironment, without the need for external networks, but at other timesinformation can be sent to and from a central location. This allows theindustrial environment to use the benefits of networking and controltechnologies, while also providing security, such as preventingcyber-attacks. The MANET 20 may use cognitive radio technologies 40,including those that form up an equivalent to the IP protocol, such asrouter 42, MAC 44, and physical layer technologies 46. In certainembodiments, the system depicted in FIGS. 1 through 5 providesnetwork-sensitive or network-aware transport of data over the network toand from a data collection device or a heavy industrial machine.

FIGS. 3-4 depict intelligent data collection technologies deployedlocally, at the edge of an IoT deployment, where heavy industrialmachines are located. This includes various sensors 52, IoT devices 54,data storage capabilities (e.g., data pools 60, or distributed ledger62) (including intelligent, self-organizing storage), sensor fusion(including self-organizing sensor fusion), and the like. Interfaces fordata collection, including multi-sensory interfaces, tablets,smartphones 58, and the like are shown. FIG. 3 also shows data pools 60that may collect data published by machines or sensors that detectconditions of machines, such as for later consumption by local or remoteintelligence. A distributed ledger system 62 may distribute storageacross the local storage of various elements of the environment, or morebroadly throughout the system. FIG. 4 also shows on-device sensor fusion80, such as for storing on a device data from multiple analog sensors82, which may be analyzed locally or in the cloud, such as by machinelearning 84, including by training a machine based on initial modelscreated by humans that are augmented by providing feedback (such asbased on measures of success) when operating the methods and systemsdisclosed herein.

FIG. 1 depicts a server based portion of an industrial IoT system thatmay be deployed in the cloud or on an enterprise owner's or operator'spremises. The server portion includes network coding (includingself-organizing network coding and/or automated configuration) that mayconfigure a network coding model based on feedback measures, networkconditions, or the like, for highly efficient transport of large amountsof data across the network to and from data collection systems and thecloud. Network coding may provide a wide range of capabilities forintelligence, analytics, remote control, remote operation, remoteoptimization, various storage configurations and the like, as depictedin FIG. 1. The various storage configurations may include distributedledger storage for supporting transactional data or other elements ofthe system.

FIG. 5 depicts a programmatic data marketplace 70, which may be aself-organizing marketplace, such as for making available data that iscollected in industrial environments, such as from data collectors, datapools, distributed ledgers, and other elements disclosed herein.Additional detail on the various components and sub-components of FIGS.1 through 5 is provided throughout this disclosure.

With reference to FIG. 6, an embodiment of platform 100 may include alocal data collection system 102, which may be disposed in anenvironment 104, such as an industrial environment similar to that shownin FIG. 3, for collecting data from or about the elements of theenvironment, such as machines, components, systems, sub-systems, ambientconditions, states, workflows, processes, and other elements. Theplatform 100 may connect to or include portions of the industrial IoTdata collection, monitoring and control system 10 depicted in FIGS. 1-5.The platform 100 may include a network data transport system 108, suchas for transporting data to and from the local data collection system102 over a network 110, such as to a host processing system 112, such asone that is disposed in a cloud computing environment or on the premisesof an enterprise, or that consists of distributed components thatinteract with each other to process data collected by the local datacollection system 102. The host processing system 112, referred to forconvenience in some cases as the host system 112, may include varioussystems, components, methods, processes, facilities, and the like forenabling automated, or automation-assisted processing of the data, suchas for monitoring one or more environments 104 or networks 110 or forremotely controlling one or more elements in a local environment 104 orin a network 110. The platform 100 may include one or more localautonomous systems, such as for enabling autonomous behavior, such asreflecting artificial, or machine-based intelligence or such as enablingautomated action based on the applications of a set of rules or modelsupon input data from the local data collection system 102 or from one ormore input sources 116, which may comprise information feeds and inputsfrom a wide array of sources, including those in the local environment104, in a network 110, in the host system 112, or in one or moreexternal systems, databases, or the like. The platform 100 may includeone or more intelligent systems 118, which may be disposed in,integrated with, or acting as inputs to one or more components of theplatform 100. Details of these and other components of the platform 100are provided throughout this disclosure.

Intelligent systems 118 may include cognitive systems 120, such asenabling a degree of cognitive behavior as a result of the coordinationof processing elements, such as mesh, peer-to-peer, ring, serial, andother architectures, where one or more node elements is coordinated withother node elements to provide collective, coordinated behavior toassist in processing, communication, data collection, or the like. TheMANET 20 depicted in FIG. 2 may also use cognitive radio technologies,including those that form up an equivalent to the IP protocol, such asrouter 42, MAC 44, and physical layer technologies 46. In one example,the cognitive system technology stack can include examples disclosed inU.S. Pat. No. 8,060,017 to Schlicht et al., issued 15 Nov. 2011 andhereby incorporated by reference as if fully set forth herein.

Intelligent systems may include machine learning systems 122, such asfor learning on one or more data sets. The one or more data sets mayinclude information collected using local data collection systems 102 orother information from input sources 116, such as to recognize states,objects, events, patterns, conditions, or the like that may, in turn, beused for processing by the host system 112 as inputs to components ofthe platform 100 and portions of the industrial IoT data collection,monitoring and control system 10, or the like. Learning may behuman-supervised or fully-automated, such as using one or more inputsources 116 to provide a data set, along with information about the itemto be learned. Machine learning may use one or more models, rules,semantic understandings, workflows, or other structured orsemi-structured understanding of the world, such as for automatedoptimization of control of a system or process based on feedback or feedforward to an operating model for the system or process. One suchmachine learning technique for semantic and contextual understandings,workflows, or other structured or semi-structured understandings isdisclosed in U.S. Pat. No. 8,200,775 to Moore, issued 12 Jun. 2012, andhereby incorporated by reference as if fully set forth herein. Machinelearning may be used to improve the foregoing, such as by adjusting oneor more weights, structures, rules, or the like (such as changing afunction within a model) based on feedback (such as regarding thesuccess of a model in a given situation) or based on iteration (such asin a recursive process). Where sufficient understanding of theunderlying structure or behavior of a system is not known, insufficientdata is not available, or in other cases where preferred for variousreasons, machine learning may also be undertaken in the absence of anunderlying model; that is, input sources may be weighted, structured, orthe like within a machine learning facility without regard to any apriori understanding of structure, and outcomes (such as those based onmeasures of success at accomplishing various desired objectives) can beserially fed to the machine learning system to allow it to learn how toachieve the targeted objectives. For example, the system may learn torecognize faults, to recognize patterns, to develop models or functions,to develop rules, to optimize performance, to minimize failure rates, tooptimize profits, to optimize resource utilization, to optimize flow(such as flow of traffic), or to optimize many other parameters that maybe relevant to successful outcomes (such as outcomes in a wide range ofenvironments). Machine learning may use genetic programming techniques,such as promoting or demoting one or more input sources, structures,data types, objects, weights, nodes, links, or other factors based onfeedback (such that successful elements emerge over a series ofgenerations). For example, alternative available sensor inputs for adata collection system 102 may be arranged in alternative configurationsand permutations, such that the system may, using generic programmingtechniques over a series of data collection events, determine whatpermutations provide successful outcomes based on various conditions(such as conditions of components of the platform 100, conditions of thenetwork 110, conditions of a data collection system 102, conditions ofan environment 104), or the like. In embodiments, local machine learningmay turn on or off one or more sensors in a multi-sensor data collectionsystem 102 in permutations over time, while tracking success outcomessuch as contributing to success in predicting a failure, contributing toa performance indicator (such as efficiency, effectiveness, return oninvestment, yield, or the like), contributing to optimization of one ormore parameters, identification of a pattern (such as relating to athreat, a failure mode, a success mode, or the like) or the like. Forexample, a system may learn what sets of sensors should be turned on oroff under given conditions to achieve the highest value utilization of adata collection system 102. In embodiments, similar techniques may beused to handle optimization of transport of data in the platform 100(such as in the network 110) by using generic programming or othermachine learning techniques to learn to configure network elements (suchas configuring network transport paths, configuring network coding typesand architectures, configuring network security elements), and the like.

In embodiments, the local data collection system 102 may include ahigh-performance, multi-sensor data collector having a number of novelfeatures for collection and processing of analog and other sensor data.In embodiments, a local data collection system 102 may be deployed tothe industrial facilities depicted in FIG. 3. A local data collectionsystem 102 may also be deployed monitor other machines such as themachine 2300 in FIG. 9 and FIG. 10, the machines 2400, 2600, 2800, 2950,3000 depicted in FIG. 12, and the machines 3202, 3204 depicted in FIG.13. The data collection system 102 may have on-board intelligent systems118 (such as for learning to optimize the configuration and operation ofthe data collector, such as configuring permutations and combinations ofsensors based on contexts and conditions). In one example, the datacollection system 102 includes a crosspoint switch 130 or other analogswitch. Automated, intelligent configuration of the local datacollection system 102 may be based on a variety of types of information,such as information from various input sources, including those based onavailable power, power requirements of sensors, the value of the datacollected (such as based on feedback information from other elements ofthe platform 100), the relative value of information (such as valuesbased on the availability of other sources of the same or similarinformation), power availability (such as for powering sensors), networkconditions, ambient conditions, operating states, operating contexts,operating events, and many others.

FIG. 7 shows elements and sub-components of a data collection andanalysis system 1100 for sensor data (such as analog sensor data)collected in industrial environments. As depicted in FIG. 7, embodimentsof the methods and systems disclosed herein may include hardware thathas several different modules starting with the multiplexer (“MUX”) mainboard 1104. In embodiments, there may be a MUX option board 1108. TheMUX main board 1104 is where the sensors connect to the system. Theseconnections are on top to enable ease of installation. Then there arenumerous settings on the underside of this board as well as on the Muxoption board 1108, which attaches to the MUX main board 1104 via twoheaders one at either end of the board. In embodiments, the Mux optionboard has the male headers, which mesh together with the female headeron the main Mux board. This enables them to be stacked on top of eachother taking up less real estate.

In embodiments, the main Mux board and/or the MUX option board thenconnects to the mother (e.g., with 4 simultaneous channels) and daughter(e.g., with 4 additional channels for 8 total channels) analog boards1110 via cables where some of the signal conditioning (such as hardwareintegration) occurs. The signals then move from the analog boards 1110to an anti-aliasing board (not shown) where some of the potentialaliasing is removed. The rest of the aliasing removal is done on thedelta sigma board 1112. The delta sigma board 1112 provides morealiasing protection along with other conditioning and digitizing of thesignal. Next, the data moves to the Jennic™ board 1114 for moredigitizing as well as communication to a computer via USB or Ethernet.In embodiments, the Jennic™ board 1114 may be replaced with a pic board1118 for more advanced and efficient data collection as well ascommunication. Once the data moves to the computer software 1102, thecomputer software 1102 can manipulate the data to show trending,spectra, waveform, statistics, and analytics.

In embodiments, the system is meant to take in all types of data fromvolts to 4-20 mA signals. In embodiments, open formats of data storageand communication may be used. In some instances, certain portions ofthe system may be proprietary especially some of research and dataassociated with the analytics and reporting. In embodiments, smart bandanalysis is a way to break data down into easily analyzed parts that canbe combined with other smart bands to make new more simplified yetsophisticated analytics. In embodiments, this unique information istaken and graphics are used to depict the conditions because picturedepictions are more helpful to the user. In embodiments, complicatedprograms and user interfaces are simplified so that any user canmanipulate the data like an expert.

In embodiments, the system in essence, works in a big loop. The systemstarts in software with a general user interface (“GUI”) 1124. Inembodiments, rapid route creation may take advantage of hierarchicaltemplates. In embodiments, a GUI is created so any general user canpopulate the information itself with simple templates. Once thetemplates are created the user can copy and paste whatever the userneeds. In addition, users can develop their own templates for futureease of use and to institutionalize the knowledge. When the user hasentered all of the user's information and connected all of the user'ssensors, the user can then start the system acquiring data.

Embodiments of the methods and systems disclosed herein may includeunique electrostatic protection for trigger and vibration inputs. Inmany critical industrial environments where large electrostatic forces,which can harm electrical equipment, may build up, for example rotatingmachinery or low-speed balancing using large belts, proper transducerand trigger input protection is required. In embodiments, a low-cost butefficient method is described for such protection without the need forexternal supplemental devices.

Typically, vibration data collectors are not designed to handle largeinput voltages due to the expense and the fact that, more often thannot, it is not needed. A need exists for these data collectors toacquire many varied types of RPM data as technology improves andmonitoring costs plummet. In embodiments, a method is using the alreadyestablished OptoMOS™ technology which permits the switching up front ofhigh voltage signals rather than using more conventional reed-relayapproaches. Many historic concerns regarding non-linear zero crossing orother non-linear solid-state behaviors have been eliminated with regardto the passing through of weakly buffered analog signals. In addition,in embodiments, printed circuit board routing topologies place all ofthe individual channel input circuitry as close to the input connectoras possible. In embodiments, a unique electrostatic protection fortrigger and vibration inputs may be placed upfront on the Mux and DAQhardware in order to dissipate the built up electric charge as thesignal passed from the sensor to the hardware. In embodiments, the Muxand analog board may support high-amperage input using a design topologycomprising wider traces and solid state relays for upfront circuitry.

In some systems multiplexers are afterthoughts and the quality of thesignal coming from the multiplexer is not considered. As a result of apoor quality multiplexer, the quality of the signal can drop as much as30 dB or more. Thus, substantial signal quality may be lost using a24-bit DAQ that has a signal to noise ratio of 110 dB and if the signalto noise ratio drops to 80 dB in the Mux, it may not be much better thana 16-bit system from 20 years ago. In embodiments of this system, animportant part at the front of the Mux is upfront signal conditioning onMux for improved signal-to-noise ratio. Embodiments may perform signalconditioning (such as range/gain control, integration, filtering, etc.)on vibration as well as other signal inputs up front before Muxswitching to achieve the highest signal-to-noise ratio.

In embodiments, in addition to providing a better signal, themultiplexer may provide a continuous monitor alarming feature. Trulycontinuous systems monitor every sensor all the time but tend to beexpensive. Typical multiplexer systems only monitor a set number ofchannels at one time and switch from bank to bank of a larger set ofsensors. As a result, the sensors not being currently collected are notbeing monitored; if a level increases the user may never know. Inembodiments, a multiplexer may have a continuous monitor alarmingfeature by placing circuitry on the multiplexer that can measure inputchannel levels against known alarm conditions even when the dataacquisition (“DAQ”) is not monitoring the input. In embodiments,continuous monitoring Mux bypass offers a mechanism whereby channels notbeing currently sampled by the Mux system may be continuously monitoredfor significant alarm conditions via a number of trigger conditionsusing filtered peak-hold circuits or functionally similar that are inturn passed on to the monitoring system in an expedient manner usinghardware interrupts or other means. This, in essence, makes the systemcontinuously monitoring, although without the ability to instantlycapture data on the problem like a true continuous system. Inembodiments, coupling this capability to alarm with adaptive schedulingtechniques for continuous monitoring and the continuous monitoringsystem's software adapting and adjusting the data collection sequencebased on statistics, analytics, data alarms and dynamic analysis mayallow the system to quickly collect dynamic spectral data on thealarming sensor very soon after the alarm sounds.

Another restriction of typical multiplexers is that they may have alimited number of channels. In embodiments, use of distributed complexprogrammable logic device (“CPLD”) chips with dedicated bus for logiccontrol of multiple Mux and data acquisition sections enables a CPLD tocontrol multiple mux and DAQs so that there is no limit to the number ofchannels a system can handle. Interfacing to multiple types ofpredictive maintenance and vibration transducers requires a great dealof switching. This includes AC/DC coupling, 4-20 interfacing, integratedelectronic piezoelectric transducer, channel power-down (for conservingop-amp power), single-ended or differential grounding options, and soon. Also required is the control of digital pots for range and gaincontrol, switches for hardware integration, AA filtering and triggering.This logic can be performed by a series of CPLD chips strategicallylocated for the tasks they control. A single giant CPLD requires longcircuit routes with a great deal of density at the single giant CPLD. Inembodiments, distributed CPLDs not only address these concerns but offera great deal of flexibility. A bus is created where each CPLD that has afixed assignment has its own unique device address. In embodiments,multiplexers and DAQs can stack together offering additional input andoutput channels to the system. For multiple boards (e.g., for multipleMux boards), jumpers are provided for setting multiple addresses. Inanother example, three bits permit up to 8 boards that are jumperconfigurable. In embodiments, a bus protocol is defined such that eachCPLD on the bus can either be addressed individually or as a group.

Typical multiplexers may be limited to collecting only sensors in thesame bank. For detailed analysis, this may be limiting as there istremendous value in being able to simultaneously review data fromsensors on the same machine. Current systems using conventional fixedbank multiplexers can only compare a limited number of channels (basedon the number of channels per bank) that were assigned to a particulargroup at the time of installation. The only way to provide someflexibility is to either overlap channels or incorporate lots ofredundancy in the system both of which can add considerable expense (insome cases an exponential increase in cost versus flexibility). Thesimplest Mux design selects one of many inputs and routes it into asingle output line. A banked design would consist of a group of thesesimple building blocks, each handling a fixed group of inputs androuting to its respective output. Typically, the inputs are notoverlapping so that the input of one Mux grouping cannot be routed intoanother. Unlike conventional Mux chips which typically switch a fixedgroup or banks of a fixed selection of channels into a single output(e.g., in groups of 2, 4, 8, etc.), a cross point Mux allows the user toassign any input to any output. Previously, crosspoint multiplexers wereused for specialized purposes such as RGB digital video applications andwere as a practical matter too noisy for analog applications such asvibration analysis; however more recent advances in the technology nowmake it feasible. Another advantage of the crosspoint Mux is the abilityto disable outputs by putting them into a high impedance state. This isideal for an output bus so that multiple Mux cards may be stacked, andtheir output buses joined together without the need for bus switches.

In embodiments, this may be addressed by use of an analog crosspointswitch for collecting variable groups of vibration input channels andproviding a matrix circuit so the system may access any set of eightchannels from the total number of input sensors.

In embodiments, the ability to control multiple multiplexers with use ofdistributed CPLD chips with dedicated bus for logic control of multipleMux and data acquisition sections is enhanced with a hierarchicalmultiplexer which allows for multiple DAQ to collect data from multiplemultiplexers. A hierarchical Mux may allow modularly output of morechannels, such as 16, 24 or more to multiple of eight channel card sets.In embodiments, this allows for faster data collection as well as morechannels of simultaneous data collection for more complex analysis. Inembodiments, the Mux may be configured slightly to make it portable anduse data acquisition parking features, which turns SV3X DAQ into aprotected system embodiment.

In embodiments, once the signals leave the multiplexer and hierarchicalMux they move to the analog board where there are other enhancements. Inembodiments, power saving techniques may be used such as: power-down ofanalog channels when not in use; powering down of component boards;power-down of analog signal processing op-amps for non-selectedchannels; powering down channels on the mother and the daughter analogboards. The ability to power down component boards and other hardware bythe low-level firmware for the DAQ system makes high-level applicationcontrol with respect to power-saving capabilities relatively easy.Explicit control of the hardware is always possible but not required bydefault. In embodiments, this power saving benefit may be of value to aprotected system, especially if it is battery operated or solar powered.

In embodiments, in order to maximize the signal to noise ratio andprovide the best data, a peak-detector for auto-scaling routed into aseparate A/D will provide the system the highest peak in each set ofdata so it can rapidly scale the data to that peak. For vibrationanalysis purposes, the built-in A/D convertors in many microprocessorsmay be inadequate with regards to number of bits, number of channels orsampling frequency versus not slowing the microprocessor downsignificantly. Despite these limitations, it is useful to use them forpurposes of auto-scaling. In embodiments, a separate A/D may be usedthat has reduced functionality and is cheaper. For each channel ofinput, after the signal is buffered (usually with the appropriatecoupling: AC or DC) but before it is signal conditioned, the signal isfed directly into the microprocessor or low-cost A/D. Unlike theconditioned signal for which range, gain and filter switches are thrown,no switches are varied. This permits the simultaneous sampling of theauto-scaling data while the input data is signal conditioned, fed into amore robust external A/D, and directed into on-board memory using directmemory access (DMA) methods where memory is accessed without requiring aCPU. This significantly simplifies the auto-scaling process by nothaving to throw switches and then allow for settling time, which greatlyslows down the auto-scaling process. Furthermore, the data may becollected simultaneously, which assures the best signal-to-noise ratio.The reduced number of bits and other features is usually more thanadequate for auto-scaling purposes. In embodiments, improved integrationusing both analog and digital methods create an innovative hybridintegration which also improves or maintains the highest possible signalto noise ratio.

In embodiments, a section of the analog board may allow routing of atrigger channel, either raw or buffered, into other analog channels.This may allow a user to route the trigger to any of the channels foranalysis and trouble shooting. Systems may have trigger channels for thepurposes of determining relative phase between various input data setsor for acquiring significant data without the needless repetition ofunwanted input. In embodiments, digitally controlled relays may be usedto switch either the raw or buffered trigger signal into one of theinput channels. It may be desirable to examine the quality of thetriggering pulse because it may be corrupted for a variety of reasonsincluding inadequate placement of the trigger sensor, wiring issues,faulty setup issues such as a dirty piece of reflective tape if using anoptical sensor, and so on. The ability to look at either the raw orbuffered signal may offer an excellent diagnostic or debugging vehicle.It also can offer some improved phase analysis capability by making useof the recorded data signal for various signal processing techniquessuch as variable speed filtering algorithms.

In embodiments, once the signals leave the analog board, the signalsmove into the delta-sigma board where precise voltage reference for A/Dzero reference offers more accurate direct current sensor data. Thedelta sigma's high speeds also provide for using higher inputoversampling for delta-sigma A/D for lower sampling rate outputs tominimize antialiasing filter requirements. Lower oversampling rates canbe used for higher sampling rates. For example, a 3^(rd) order AA filterset for the lowest sampling requirement for 256 Hz (Fmax of 100 Hz) isthen adequate for Fmax ranges of 200 and 500 Hz. Another higher-cutoffAA filter can then be used for Fmax ranges from 1 kHz and higher (with asecondary filter kicking in at 2.56× the highest sampling rate of 128kHz). In embodiments, a CPLD may be used as a clock-divider for adelta-sigma A/D to achieve lower sampling rates without the need fordigital resampling. In embodiments, a high-frequency crystal referencecan be divided down to lower frequencies by employing a CPLD as aprogrammable clock divider. The accuracy of the divided down lowerfrequencies is even more accurate than the original source relative totheir longer time periods. This also minimizes or removes the need forresampling processing by the delta-sigma A/D.

In embodiments, the data then moves from the delta-sigma board to theJennic™ board where phase relative to input and trigger channels usingon-board timers may be digitally derived. In embodiments, the Jennic™board also has the ability to store calibration data and systemmaintenance repair history data in an on-board card set. In embodiments,the Jennic™ board will enable acquiring long blocks of data athigh-sampling rate as opposed to multiple sets of data taken atdifferent sampling rates so it can stream data and acquire long blocksof data for advanced analysis in the future.

In embodiments, after the signal moves through the Jennic™ board it maythen be transmitted to the computer. In embodiments, the computersoftware will be used to add intelligence to the system starting with anexpert system GUI. The GUI will offer a graphical expert system withsimplified user interface for defining smart bands and diagnoses whichfacilitate anyone to develop complex analytics. In embodiments, thisuser interface may revolve around smart bands, which are a simplifiedapproach to complex yet flexible analytics for the general user. Inembodiments, the smart bands may pair with a self-learning neuralnetwork for an even more advanced analytical approach. In embodiments,this system may use the machine's hierarchy for additional analyticalinsight. One critical part of predictive maintenance is the ability tolearn from known information during repairs or inspections. Inembodiments, graphical approaches for back calculations may improve thesmart bands and correlations based on a known fault or problem.

In embodiments, there is a smart route which adapts which sensors itcollects simultaneously in order to gain additional correlativeintelligence. In embodiments, smart operational data store (“ODS”)allows the system to elect to gather data to perform operationaldeflection shape analysis in order to further examine the machinerycondition. In embodiments, adaptive scheduling techniques allow thesystem to change the scheduled data collected for full spectral analysisacross a number (e.g., eight), of correlative channels. In embodiments,the system may provide data to enable extended statistics capabilitiesfor continuous monitoring as well as ambient local vibration foranalysis that combines ambient temperature and local temperature andvibration levels changes for identifying machinery issues.

In embodiments, a data acquisition device may be controlled by apersonal computer (PC) to implement the desired data acquisitioncommands. In embodiments, the DAQ box may be self-sufficient and canacquire, process, analyze and monitor independent of external PCcontrol. Embodiments may include secure digital (SD) card storage. Inembodiments, significant additional storage capability may be providedby utilizing an SD card. This may prove critical for monitoringapplications where critical data may be stored permanently. Also, if apower failure should occur, the most recent data may be stored despitethe fact that it was not off-loaded to another system.

A current trend has been to make DAQ systems as communicative aspossible with the outside world usually in the form of networksincluding wireless. In the past it was common to use a dedicated bus tocontrol a DAQ system with either a microprocessor ormicrocontroller/microprocessor paired with a PC. In embodiments, a DAQsystem may comprise one or more microprocessor/microcontrollers,specialized microcontrollers/microprocessors, or dedicated processorsfocused primarily on the communication aspects with the outside world.These include USB, Ethernet and wireless with the ability to provide anIP address or addresses in order to host a webpage. All communicationswith the outside world are then accomplished using a simple text basedmenu. The usual array of commands (in practice more than a hundred) suchas InitializeCard, AcquireData, StopAcquisition, RetrieveCalibrationInfo, and so on, would be provided.

In embodiments, intense signal processing activities includingresampling, weighting, filtering, and spectrum processing may beperformed by dedicated processors such as field-programmable gate array(“FPGAs”), digital signal processor (“DSP”), microprocessors,micro-controllers, or a combination thereof. In embodiments, thissubsystem may communicate via a specialized hardware bus with thecommunication processing section. It will be facilitated with dual-portmemory, semaphore logic, and so on. This embodiment will not onlyprovide a marked improvement in efficiency but can significantly improvethe processing capability, including the streaming of the data as wellother high-end analytical techniques. This negates the need forconstantly interrupting the main processes which include the control ofthe signal conditioning circuits, triggering, raw data acquisition usingthe A/D, directing the A/D output to the appropriate on-board memory andprocessing that data.

Embodiments may include sensor overload identification. A need existsfor monitoring systems to identify when the sensor is overloading. Theremay be situations involving high-frequency inputs that will saturate astandard 100 mv/g sensor (which is most commonly used in the industry)and having the ability to sense the overload improves data quality forbetter analysis. A monitoring system may identify when their system isoverloading, but in embodiments, the system may look at the voltage ofthe sensor to determine if the overload is from the sensor, enabling theuser to get another sensor better suited to the situation, or gather thedata again.

Embodiments may include radio frequency identification (“RFID”) and aninclinometer or accelerometer on a sensor so the sensor can indicatewhat machine/bearing it is attached to and what direction such that thesoftware can automatically store the data without the user input. Inembodiments, users could put the system on any machine or machines andthe system would automatically set itself up and be ready for datacollection in seconds.

Embodiments may include ultrasonic online monitoring by placingultrasonic sensors inside transformers, motor control centers, breakersand the like and monitoring, via a sound spectrum, continuously lookingfor patterns that identify arcing, corona and other electrical issuesindicating a break down or issue. Embodiments may include providingcontinuous ultrasonic monitoring of rotating elements and bearings of anenergy production facility. In embodiments, an analysis engine may beused in ultrasonic online monitoring as well as identifying other faultsby combining the ultrasonic data with other parameters such asvibration, temperature, pressure, heat flux, magnetic fields, electricalfields, currents, voltage, capacitance, inductance, and combinations(e.g., simple ratios) of the same, among many others.

Embodiments of the methods and systems disclosed herein may include useof an analog crosspoint switch for collecting variable groups ofvibration input channels. For vibration analysis, it is useful to obtainmultiple channels simultaneously from vibration transducers mounted ondifferent parts of a machine (or machines) in multiple directions. Byobtaining the readings at the same time, for example, the relativephases of the inputs may be compared for the purpose of diagnosingvarious mechanical faults. Other types of cross channel analyses such ascross-correlation, transfer functions, Operating Deflection Shape(“ODS”) may also be performed.

Embodiments of the methods and systems disclosed herein may includeprecise voltage reference for A/D zero reference. Some A/D chips providetheir own internal zero voltage reference to be used as a mid-scalevalue for external signal conditioning circuitry to ensure that both theA/D and external op-amps use the same reference. Although this soundsreasonable in principle, there are practical complications. In manycases these references are inherently based on a supply voltage using aresistor-divider. For many current systems, especially those whose poweris derived from a PC via USB or similar bus, this provides for anunreliable reference, as the supply voltage will often vary quitesignificantly with load. This is especially true for delta-sigma A/Dchips which necessitate increased signal processing. Although theoffsets may drift together with load, a problem arises if one wants tocalibrate the readings digitally. It is typical to modify the voltageoffset expressed as counts coming from the A/D digitally to compensatefor the DC drift. However, for this case, if the proper calibrationoffset is determined for one set of loading conditions, they will notapply for other conditions. An absolute DC offset expressed in countswill no longer be applicable. As a result, it becomes necessary tocalibrate for all loading conditions which becomes complex, unreliable,and ultimately unmanageable. In embodiments, an external voltagereference is used which is simply independent of the supply voltage touse as the zero offset.

In embodiments, the system provides a phase-lock-loop band pass trackingfilter method for obtaining slow-speed RPMs and phase for balancingpurposes to remotely balance slow speed machinery, such as in papermills, as well as offering additional analysis from its data. Forbalancing purposes, it is sometimes necessary to balance at very slowspeeds. A typical tracking filter may be constructed based on aphase-lock loop or PLL design; however, stability and speed range areoverriding concerns. In embodiments, a number of digitally controlledswitches are used for selecting the appropriate RC and dampingconstants. The switching can be done all automatically after measuringthe frequency of the incoming tach signal. Embodiments of the methodsand systems disclosed herein may include digital derivation of phaserelative to input and trigger channels using on-board timers. Inembodiments, digital phase derivation uses digital timers to ascertainan exact delay from a trigger event to the precise start of dataacquisition. This delay, or offset, then, is further refined usinginterpolation methods to obtain an even more precise offset which isthen applied to the analytically determined phase of the acquired datasuch that the phase is “in essence” an absolute phase with precisemechanical meaning useful for among other things, one-shot balancing,alignment analysis, and so on.

Embodiments of the methods and systems disclosed herein may includesignal processing firmware/hardware. In embodiments, long blocks of datamay be acquired at high-sampling rate as opposed to multiple sets ofdata taken at different sampling rates. Typically, in modern routecollection for vibration analysis, it is customary to collect data at afixed sampling rate with a specified data length. The sampling rate anddata length may vary from route point to point based on the specificmechanical analysis requirements at hand. For example, a motor mayrequire a relatively low sampling rate with high resolution todistinguish running speed harmonics from line frequency harmonics. Thepractical trade-off here though is that it takes more collection time toachieve this improved resolution. In contrast, some high-speedcompressors or gear sets require much higher sampling rates to measurethe amplitudes of relatively higher frequency data although the preciseresolution may not be as necessary. Ideally, however, it would be betterto collect a very long sample length of data at a very high-samplingrate. When digital acquisition devices were first popularized in theearly 1980's, the A/D sampling, digital storage, and computationalabilities were not close to what they are today, so compromises weremade between the time required for data collection and the desiredresolution and accuracy. It was because of this limitation that someanalysts in the field even refused to give up their analog taperecording systems, which did not suffer as much from these samedigitizing drawbacks. A few hybrid systems were employed that woulddigitize the play back of the recorded analog data at multiple samplingrates and lengths desired, though these systems were admittedly lessautomated. The more common approach, as mentioned earlier, is to balancedata collection time with analysis capability and digitally acquire thedata blocks at multiple sampling rates and sampling lengths anddigitally store these blocks separately. In embodiments, a long datalength of data can be collected at the highest practical sampling rate(e.g., 102.4 kHz; corresponding to a 40 kHz Fmax) and stored. This longblock of data can be acquired in the same amount of time as the shorterlength of the lower sampling rates utilized by a priori methods so thatthere is no effective delay added to the sampling at the measurementpoint, always a concern in route collection. In embodiments, analog taperecording of data is digitally simulated with such a precision that itcan be in effect considered continuous or “analog” for many purposes,including for purposes of embodiments of the present disclosure, exceptwhere context indicates otherwise.

Embodiments of the methods and systems disclosed herein may includestorage of calibration data and maintenance history on-board card sets.Many data acquisition devices which rely on interfacing to a PC tofunction store their calibration coefficients on the PC. This isespecially true for complex data acquisition devices whose signal pathsare many and therefore whose calibration tables can be quite large. Inembodiments, calibration coefficients are stored in flash memory whichwill remember this data or any other significant information for thatmatter, for all practical purposes, permanently. This information mayinclude nameplate information such as serial numbers of individualcomponents, firmware or software version numbers, maintenance history,and the calibration tables. In embodiments, no matter which computer thebox is ultimately connected to, the DAQ box remains calibrated andcontinues to hold all of this critical information. The PC or externaldevice may poll for this information at any time for implantation orinformation exchange purposes.

Embodiments of the methods and systems disclosed herein may includerapid route creation taking advantage of hierarchical templates. In thefield of vibration monitoring, as well as parametric monitoring ingeneral, it is necessary to establish in a database or functionalequivalent the existence of data monitoring points. These points areassociated a variety of attributes including the following categories:transducer attributes, data collection settings, machinery parametersand operating parameters. The transducer attributes would include probetype, probe mounting type and probe mounting direction or axisorientation. Data collection attributes associated with the measurementwould involve a sampling rate, data length, integrated electronicpiezoelectric probe power and coupling requirements, hardwareintegration requirements, 4-20 or voltage interfacing, range and gainsettings (if applicable), filter requirements, and so on. Machineryparametric requirements relative to the specific point would includesuch items as operating speed, bearing type, bearing parametric datawhich for a rolling element bearing includes the pitch diameter, numberof balls, inner race, and outer-race diameters. For a tilting padbearing, this would include the number of pads and so on. Formeasurement points on a piece of equipment such as a gearbox, neededparameters would include, for example, the number of gear teeth on eachof the gears. For induction motors, it would include the number of rotorbars and poles; for compressors, the number of blades and/or vanes; forfans, the number of blades. For belt/pulley systems, the number of beltsas well as the relevant belt-passing frequencies may be calculated fromthe dimensions of the pulleys and pulley center-to-center distance. Formeasurements near couplings, the coupling type and number of teeth in ageared coupling may be necessary, and so on. Operating parametric datawould include operating load, which may be expressed in megawatts, flow(either air or fluid), percentage, horsepower, feet-per-minute, and soon. Operating temperatures both ambient and operational, pressures,humidity, and so on, may also be relevant. As can be seen, the setupinformation required for an individual measurement point can be quitelarge. It is also crucial to performing any legitimate analysis of thedata. Machinery, equipment, and bearing specific information areessential for identifying fault frequencies as well as anticipating thevarious kinds of specific faults to be expected. The transducerattributes as well as data collection parameters are vital for properlyinterpreting the data along with providing limits for the type ofanalytical techniques suitable. The traditional means of entering thisdata has been manual and quite tedious, usually at the lowesthierarchical level (for example, at the bearing level with regards tomachinery parameters), and at the transducer level for data collectionsetup information. It cannot be stressed enough, however, the importanceof the hierarchical relationships necessary to organize data—both foranalytical and interpretive purposes as well as the storage and movementof data. Here, we are focusing primarily on the storage and movement ofdata. By its nature, the aforementioned setup information is extremelyredundant at the level of the lowest hierarchies; however, because ofits strong hierarchical nature, it can be stored quite efficiently inthat form. In embodiments, hierarchical nature can be utilized whencopying data in the form of templates. As an example, hierarchicalstorage structure suitable for many purposes is defined from general tospecific of company, plant or site, unit or process, machine, equipment,shaft element, bearing, and transducer. It is much easier to copy dataassociated with a particular machine, piece of equipment, shaft elementor bearing than it is to copy only at the lowest transducer level. Inembodiments, the system not only stores data in this hierarchicalfashion, but robustly supports the rapid copying of data using thesehierarchical templates. Similarity of elements at specific hierarchicallevels lends itself to effective data storage in hierarchical format.For example, so many machines have common elements such as motors,gearboxes, compressors, belts, fans, and so on. More specifically, manymotors can be easily classified as induction, DC, fixed or variablespeed. Many gearboxes can be grouped into commonly occurring groupingssuch as input/output, input pinion/intermediate pinion/output pinion,4-posters, and so on. Within a plant or company, there are many similartypes of equipment purchased and standardized on for both cost andmaintenance reasons. This results in an enormous overlapping of similartypes of equipment and, as a result, offers a great opportunity fortaking advantage of a hierarchical template approach.

Embodiments of the methods and systems disclosed herein may includesmart bands. Smart bands refer to any processed signal characteristicsderived from any dynamic input or group of inputs for the purposes ofanalyzing the data and achieving the correct diagnoses. Furthermore,smart bands may even include mini or relatively simple diagnoses for thepurposes of achieving a more robust and complex one. Historically, inthe field of mechanical vibration analysis, Alarm Bands have been usedto define spectral frequency bands of interest for the purposes ofanalyzing and/or trending significant vibration patterns. The Alarm Bandtypically consists of a spectral (amplitude plotted against frequency)region defined between a low and high frequency border. The amplitudebetween these borders is summed in the same manner for which an overallamplitude is calculated. A Smart Band is more flexible in that it notonly refers to a specific frequency band but can also refer to a groupof spectral peaks such as the harmonics of a single peak, a true-peaklevel or crest factor derived from a time waveform, an overall derivedfrom a vibration envelope spectrum or other specialized signal analysistechnique or a logical combination (AND, OR, XOR, etc.) of these signalattributes. In addition, a myriad assortment of other parametric data,including system load, motor voltage and phase information, bearingtemperature, flow rates, and the like, can likewise be used as the basisfor forming additional smart bands. In embodiments, Smart Band symptomsmay be used as building blocks for an expert system whose engine wouldutilize these inputs to derive diagnoses. Some of these mini-diagnosesmay then in turn be used as Smart-Band symptoms (smart bands can includeeven diagnoses) for more generalized diagnoses.

Embodiments of the methods and systems disclosed herein may include aneural net expert system using smart bands. Typical vibration analysisengines are rule-based (i.e., they use a list of expert rules which,when met, trigger specific diagnoses). In contrast, a neural approachutilizes the weighted triggering of multiple input stimuli into smalleranalytical engines or neurons which in turn feed a simplified weightedoutput to other neurons. The output of these neurons can be alsoclassified as smart bands which in turn feed other neurons. Thisproduces a more layered approach to expert diagnosing as opposed to theone-shot approach of a rule-based system. In embodiments, the expertsystem utilizes this neural approach using smart bands; however, it doesnot preclude rule-based diagnoses being reclassified as smart bands asfurther stimuli to be utilized by the expert system. From thispoint-of-view, it can be overviewed as a hybrid approach, although atthe highest level it is essentially neural.

Embodiments of the methods and systems disclosed herein may include useof database hierarchy in analysis smart band symptoms and diagnoses maybe assigned to various hierarchical database levels. For example, asmart band may be called “Looseness” at the bearing level, trigger“Looseness” at the equipment level, and trigger “Looseness” at themachine level. Another example would be having a smart band diagnosiscalled “Horizontal Plane Phase Flip” across a coupling and generate asmart band diagnosis of “Vertical Coupling Misalignment” at the machinelevel.

Embodiments of the methods and systems disclosed herein may includeexpert system GUIs. In embodiments, the system undertakes a graphicalapproach to defining smart bands and diagnoses for the expert system.The entry of symptoms, rules, or more generally smart bands for creatinga particular machine diagnosis, may be tedious and time consuming. Onemeans of making the process more expedient and efficient is to provide agraphical means by use of wiring. The proposed graphical interfaceconsists of four major components: a symptom parts bin, diagnoses bin,tools bin, and graphical wiring area (“GWA”). In embodiments, a symptomparts bin includes various spectral, waveform, envelope and any type ofsignal processing characteristic or grouping of characteristics such asa spectral peak, spectral harmonic, waveform true-peak, waveformcrest-factor, spectral alarm band, and so on. Each part may be assignedadditional properties. For example, a spectral peak part may be assigneda frequency or order (multiple) of running speed. Some parts may bepre-defined or user defined such as a 1×, 2×, 3× running speed, 1×, 2×,3× gear mesh, 1×, 2×, 3× blade pass, number of motor rotor bars×runningspeed, and so on.

In embodiments, the diagnoses bin includes various pre-defined as wellas user-defined diagnoses such as misalignment, imbalance, looseness,bearing faults, and so on. Like parts, diagnoses may also be used asparts for the purposes of building more complex diagnoses. Inembodiments, the tools bin includes logical operations such as AND, OR,XOR, etc. or other ways of combining the various parts listed above suchas Find Max, Find Min, Interpolate, Average, other StatisticalOperations, etc. In embodiments, a graphical wiring area includes partsfrom the parts bin or diagnoses from the diagnoses bin and may becombined using tools to create diagnoses. The various parts, tools anddiagnoses will be represented with icons which are simply graphicallywired together in the desired manner.

Embodiments of the methods and systems disclosed herein may include agraphical approach for back-calculation definition. In embodiments, theexpert system also provides the opportunity for the system to learn. Ifone already knows that a unique set of stimuli or smart bandscorresponds to a specific fault or diagnosis, then it is possible toback-calculate a set of coefficients that when applied to a future setof similar stimuli would arrive at the same diagnosis. In embodiments,if there are multiple sets of data, a best-fit approach may be used.Unlike the smart band GUI, this embodiment will self-generate a wiringdiagram. In embodiments, the user may tailor the back-propagationapproach settings and use a database browser to match specific sets ofdata with the desired diagnoses. In embodiments, the desired diagnosesmay be created or custom tailored with a smart band GUI. In embodiments,after that, a user may press the GENERATE button and a dynamic wiring ofthe symptom-to-diagnosis may appear on the screen as it works throughthe algorithms to achieve the best fit. In embodiments, when complete, avariety of statistics are presented which detail how well the mappingprocess proceeded. In some cases, no mapping may be achieved if, forexample, the input data was all zero or the wrong data (mistakenlyassigned) and so on. Embodiments of the methods and systems disclosedherein may include bearing analysis methods. In embodiments, bearinganalysis methods may be used in conjunction with a computer aided design(“CAD”), predictive deconvolution, minimum variance distortionlessresponse (“MVDR”) and spectrum sum-of-harmonics.

In recent years, there has been a strong drive to save power which hasresulted in an influx of variable frequency drives and variable speedmachinery. In embodiments, a bearing analysis method is provided. Inembodiments, torsional vibration detection and analysis is providedutilizing transitory signal analysis to provide an advanced torsionalvibration analysis for a more comprehensive way to diagnose machinerywhere torsional forces are relevant (such as machinery with rotatingcomponents). Due primarily to the decrease in cost of motor speedcontrol systems, as well as the increased cost and consciousness ofenergy-usage, it has become more economically justifiable to takeadvantage of the potentially vast energy savings of load control.Unfortunately, one frequently overlooked design aspect of this issue isthat of vibration. When a machine is designed to run at only one speed,it is far easier to design the physical structure accordingly so as toavoid mechanical resonances both structural and torsional, each of whichcan dramatically shorten the mechanical health of a machine. This wouldinclude such structural characteristics as the types of materials touse, their weight, stiffening member requirements and placement, bearingtypes, bearing location, base support constraints, etc. Even withmachines running at one speed, designing a structure so as to minimizevibration can prove a daunting task, potentially requiring computermodeling, finite-element analysis, and field testing. By throwingvariable speeds into the mix, in many cases, it becomes impossible todesign for all desirable speeds. The problem then becomes one ofminimization, e.g., by speed avoidance. This is why many modern motorcontrollers are typically programmed to skip or quickly pass throughspecific speed ranges or bands. Embodiments may include identifyingspeed ranges in a vibration monitoring system. Non-torsional, structuralresonances are typically fairly easy to detect using conventionalvibration analysis techniques. However, this is not the case fortorsion. One special area of current interest is the increased incidenceof torsional resonance problems, apparently due to the increasedtorsional stresses of speed change as well as the operation of equipmentat torsional resonance speeds. Unlike non-torsional structuralresonances which generally manifest their effect with dramaticallyincreased casing or external vibration, torsional resonances generallyshow no such effect. In the case of a shaft torsional resonance, thetwisting motion induced by the resonance may only be discernible bylooking for speed and/or phase changes. The current standard methodologyfor analyzing torsional vibration involves the use of specializedinstrumentation. Methods and systems disclosed herein allow analysis oftorsional vibration without such specialized instrumentation. This mayconsist of shutting the machine down and employing the use of straingauges and/or other special fixturing such as speed encoder platesand/or gears. Friction wheels are another alternative, but theytypically require manual implementation and a specialized analyst. Ingeneral, these techniques can be prohibitively expensive and/orinconvenient. An increasing prevalence of continuous vibrationmonitoring systems due to decreasing costs and increasing convenience(e.g., remote access) exists. In embodiments, there is an ability todiscern torsional speed and/or phase variations with just the vibrationsignal. In embodiments, transient analysis techniques may be utilized todistinguish torsionally induced vibrations from mere speed changes dueto process control. In embodiments, factors for discernment might focuson one or more of the following aspects: the rate of speed change due tovariable speed motor control would be relatively slow, sustained anddeliberate; torsional speed changes would tend to be short, impulsiveand not sustained; torsional speed changes would tend to be oscillatory,most likely decaying exponentially, process speed changes would not; andsmaller speed changes associated with torsion relative to the shaft'srotational speed which suggest that monitoring phase behavior would showthe quick or transient speed bursts in contrast to the slow phasechanges historically associated with ramping a machine's speed up ordown (as typified with Bode or Nyquist plots).

Embodiments of the methods and systems disclosed herein may includeimproved integration using both analog and digital methods. When asignal is digitally integrated using software, essentially the spectrallow-end frequency data has its amplitude multiplied by a function whichquickly blows up as it approaches zero and creates what is known in theindustry as a “ski-slope” effect. The amplitude of the ski-slope isessentially the noise floor of the instrument. The simple remedy forthis is the traditional hardware integrator, which can perform atsignal-to-noise ratios much greater than that of an already digitizedsignal. It can also limit the amplification factor to a reasonable levelso that multiplication by very large numbers is essentially prohibited.However, at high frequencies where the frequency becomes large, theoriginal amplitude which may be well above the noise floor is multipliedby a very small number (1/f) that plunges it well below the noise floor.The hardware integrator has a fixed noise floor that although low floordoes not scale down with the now lower amplitude high-frequency data. Incontrast, the same digital multiplication of a digitized high-frequencysignal also scales down the noise floor proportionally. In embodiments,hardware integration may be used below the point of unity gain where (ata value usually determined by units and/or desired signal to noise ratiobased on gain) and software integration may be used above the value ofunity gain to produce an ideal result. In embodiments, this integrationis performed in the frequency domain. In embodiments, the resultinghybrid data can then be transformed back into a waveform which should befar superior in signal-to-noise ratio when compared to either hardwareintegrated or software integrated data. In embodiments, the strengths ofhardware integration are used in conjunction with those of digitalsoftware integration to achieve the maximum signal-to-noise ratio. Inembodiments, the first order gradual hardware integrator high passfilter along with curve fitting allow some relatively low frequency datato get through while reducing or eliminating the noise, allowing veryuseful analytical data that steep filters kill to be salvaged.

Embodiments of the methods and systems disclosed herein may includeadaptive scheduling techniques for continuous monitoring. Continuousmonitoring is often performed with an up-front Mux whose purpose it isto select a few channels of data among many to feed the hardware signalprocessing, A/D, and processing components of a DAQ system. This is doneprimarily out of practical cost considerations. The tradeoff is that allof the points are not monitored continuously (although they may bemonitored to a lesser extent via alternative hardware methods). Inembodiments, multiple scheduling levels are provided. In embodiments, atthe lowest level, which is continuous for the most part, all of themeasurement points will be cycled through in round-robin fashion. Forexample, if it takes 30 seconds to acquire and process a measurementpoint and there are 30 points, then each point is serviced once every 15minutes; however, if a point should alarm by whatever criteria the userselects, its priority level can be increased so that it is serviced moreoften. As there can be multiple grades of severity for each alarm, socan there me multiple levels of priority with regards to monitoring. Inembodiments, more severe alarms will be monitored more frequently. Inembodiments, a number of additional high-level signal processingtechniques can be applied at less frequent intervals. Embodiments maytake advantage of the increased processing power of a PC and the PC cantemporarily suspend the round-robin route collection (with its multipletiers of collection) process and stream the required amount of data fora point of its choosing. Embodiments may include various advancedprocessing techniques such as envelope processing, wavelet analysis, aswell as many other signal processing techniques. In embodiments, afteracquisition of this data, the DAQ card set will continue with its routeat the point it was interrupted. In embodiments, various PC scheduleddata acquisitions will follow their own schedules which will be lessfrequency than the DAQ card route. They may be set up hourly, daily, bynumber of route cycles (for example, once every 10 cycles) and alsoincreased scheduling-wise based on their alarm severity priority or typeof measurement (e.g., motors may be monitored differently than fans).

Embodiments of the methods and systems disclosed herein may include dataacquisition parking features. In embodiments, a data acquisition boxused for route collection, real time analysis and in general as anacquisition instrument can be detached from its PC (tablet or otherwise)and powered by an external power supply or suitable battery. Inembodiments, the data collector still retains continuous monitoringcapability and its on-board firmware can implement dedicated monitoringfunctions for an extended period of time or can be controlled remotelyfor further analysis. Embodiments of the methods and systems disclosedherein may include extended statistical capabilities for continuousmonitoring.

Embodiments of the methods and systems disclosed herein may includeambient sensing plus local sensing plus vibration for analysis. Inembodiments, ambient environmental temperature and pressure, sensedtemperature and pressure may be combined with long/medium term vibrationanalysis for prediction of any of a range of conditions orcharacteristics. Variants may add infrared sensing, infraredthermography, ultrasound, and many other types of sensors and inputtypes in combination with vibration or with each other. Embodiments ofthe methods and systems disclosed herein may include a smart route. Inembodiments, the continuous monitoring system's software willadapt/adjust the data collection sequence based on statistics,analytics, data alarms and dynamic analysis. Typically, the route is setbased on the channels the sensors are attached to. In embodiments, withthe crosspoint switch, the Mux can combine any input Mux channels to the(e.g., eight) output channels. In embodiments, as channels go into alarmor the system identifies key deviations, it will pause the normal routeset in the software to gather specific simultaneous data, from thechannels sharing key statistical changes, for more advanced analysis.Embodiments include conducting a smart ODS or smart transfer function.

Embodiments of the methods and systems disclosed herein may includesmart ODS and one or more transfer functions. In embodiments, due to asystem's multiplexer and crosspoint switch, an ODS, a transfer function,or other special tests on all the vibration sensors attached to amachine/structure can be performed and show exactly how the machine'spoints are moving in relationship to each other. In embodiments, 40-50kHz and longer data lengths (e.g., at least one minute) may be streamed,which may reveal different information than what a normal ODS ortransfer function will show. In embodiments, the system will be able todetermine, based on the data/statistics/analytics to use, the smartroute feature that breaks from the standard route and conducts an ODSacross a machine, structure or multiple machines and structures thatmight show a correlation because the conditions/data directs it. Inembodiments, for the transfer functions there may be an impact hammerused on one channel and then compared against other vibration sensors onthe machine. In embodiments, the system may use the condition changessuch as load, speed, temperature or other changes in the machine orsystem to conduct the transfer function. In embodiments, differenttransfer functions may be compared to each other over time. Inembodiments, difference transfer functions may be strung together like amovie that may show how the machinery fault changes, such as a bearingthat could show how it moves through the four stages of bearing failureand so on. Embodiments of the methods and systems disclosed herein mayinclude a hierarchical Mux.

With reference to FIG. 8, the present disclosure generally includesdigitally collecting or streaming waveform data 2010 from a machine 2020whose operational speed can vary from relatively slow rotational oroscillational speeds to much higher speeds in different situations. Thewaveform data 2010, at least on one machine, may include data from asingle-axis sensor 2030 mounted at an unchanging reference location 2040and from a three-axis sensor 2050 mounted at changing locations (orlocated at multiple locations), including location 2052. In embodiments,the waveform data 2010 can be vibration data obtained simultaneouslyfrom each sensor 2030, 2050 in a gap-free format for a duration ofmultiple minutes with maximum resolvable frequencies sufficiently largeto capture periodic and transient impact events. By way of this example,the waveform data 2010 can include vibration data that can be used tocreate an operational deflecting shape. It can also be used, as needed,to diagnose vibrations from which a machine repair solution can beprescribed.

In embodiments, the machine 2020 can further include a housing 2100 thatcan contain a drive motor 2110 that can drive a shaft 2120. The shaft2120 can be supported for rotation or oscillation by a set of bearings2130, such as including a first bearing 2140 and a second bearing 2150.A data collection module 2160 can connect to (or be resident on) themachine 2020. In one example, the data collection module 2160 can belocated and accessible through a cloud network facility 2170, cancollect the waveform data 2010 from the machine 2020, and deliver thewaveform data 2010 to a remote location. A working end 2180 of the driveshaft 2120 of the machine 2020 can drive a windmill, a fan, a pump, adrill, a gear system, a drive system, or other working element, as thetechniques described herein can apply to a wide range of machines,equipment, tools, or the like that include rotating or oscillatingelements. In other instances, a generator can be substituted for themotor 2110, and the working end of the drive shaft 2120 can directrotational energy to the generator to generate power, rather thanconsume it.

In embodiments, the waveform data 2010 can be obtained using apredetermined route format based on the layout of the machine 2020. Thewaveform data 2010 may include data from the single-axis sensor 2030 andthe three-axis sensor 2050. The single-axis sensor 2030 can serve as areference probe with its one channel of data and can be fixed at theunchanging reference location 2040 on the machine under survey. Thethree-axis sensor 2050 can serve as a tri-axial probe (e.g., threeorthogonal axes) with its three channels of data and can be moved alonga predetermined diagnostic route format from one test point to the nexttest point. In one example, both sensors 2030, 2050 can be mountedmanually to the machine 2020 and can connect to a separate portablecomputer in certain service examples. The reference probe can remain atone location while the user can move the tri-axial vibration probe alongthe predetermined route, such as from bearing-to-bearing on a machine.In this example, the user is instructed to locate the sensors at thepredetermined locations to complete the survey (or portion thereof) ofthe machine.

With reference to FIG. 9, a portion of an exemplary machine 2200 isshown having a tri-axial sensor 2210 mounted to a location 2220associated with a motor bearing of the machine 2200 with an output shaft2230 and output member 2240 in accordance with the present disclosure.With reference to FIGS. 10 and 11, an exemplary machine 2300 is shownhaving a tri-axial sensor 2310 and a single-axis vibration sensor 2320serving as the reference sensor that is attached on the machine 2300 atan unchanging location for the duration of the vibration survey inaccordance with the present disclosure. The tri-axial sensor 2310 andthe single-axis vibration sensor 2320 can be connected to a datacollection system 2330.

In further examples, the sensors and data acquisition modules andequipment can be integral to, or resident on, the rotating machine. Byway of these examples, the machine can contain many single-axis sensorsand many tri-axial sensors at predetermined locations. The sensors canbe originally installed equipment and provided by the original equipmentmanufacturer or installed at a different time in a retrofit application.The data collection module 2160, or the like, can select and use onesingle-axis sensor and obtain data from it exclusively during thecollection of waveform data 2010 while moving to each of the tri-axialsensors. The data collection module 2160 can be resident on the machine2020 and/or connect via the cloud network facility 2170.

With reference to FIG. 8, the various embodiments include collecting thewaveform data 2010 by digitally recording locally, or streaming over,the cloud network facility 2170. The waveform data 2010 can be collectedso as to be gap-free with no interruptions and, in some respects, can besimilar to an analog recording of waveform data. The waveform data 2010from all of the channels can be collected for one to two minutesdepending on the rotating or oscillating speed of the machine beingmonitored. In embodiments, the data sampling rate can be at a relativelyhigh-sampling rate relative to the operating frequency of the machine2020.

In embodiments, a second reference sensor can be used, and a fifthchannel of data can be collected. As such, the single-axis sensor can bethe first channel and tri-axial vibration can occupy the second, thethird, and the fourth data channels. This second reference sensor, likethe first, can be a single-axis sensor, such as an accelerometer. Inembodiments, the second reference sensor, like the first referencesensor, can remain in the same location on the machine for the entirevibration survey on that machine. The location of the first referencesensor (i.e., the single-axis sensor) may be different than the locationof the second reference sensors (i.e., another single-axis sensor). Incertain examples, the second reference sensor can be used when themachine has two shafts with different operating speeds, with the tworeference sensors being located on the two different shafts. Inaccordance with this example, further single-axis reference sensors canbe employed at additional but different unchanging locations associatedwith the rotating machine.

In embodiments, the waveform data can be transmitted electronically in agap-free format at a significantly high rate of sampling for arelatively longer period of time. In one example, the period of time is60 seconds to 120 seconds. In another example, the rate of sampling is100 kHz with a maximum resolvable frequency (Fmax) of 40 kHz. It will beappreciated in light of this disclosure that the waveform data can beshown to approximate more closely some of the wealth of data availablefrom previous instances of analog recording of waveform data.

In embodiments, sampling, band selection, and filtering techniques canpermit one or more portions of a long stream of data (i.e., one to twominutes in duration) to be under sampled or over sampled to realizevarying effective sampling rates. To this end, interpolation anddecimation can be used to further realize varying effective samplingrates. For example, oversampling may be applied to frequency bands thatare proximal to rotational or oscillational operating speeds of thesampled machine, or to harmonics thereof, as vibration effects may tendto be more pronounced at those frequencies across the operating range ofthe machine. In embodiments, the digitally-sampled data set can bedecimated to produce a lower sampling rate. It will be appreciated inlight of the disclosure that decimate in this context can be theopposite of interpolate. In embodiments, decimating the data set caninclude first applying a low-pass filter to the digitally-sampled dataset and then undersampling the data set.

In one example, a sample waveform at 100 Hz can be undersampled at everytenth point of the digital waveform to produce an effective samplingrate of 10 Hz, but the remaining nine points of that portion of thewaveform are effectively discarded and not included in the modeling ofthe sample waveform. Moreover, this type of bare undersampling cancreate ghost frequencies due to the undersampling rate (i.e., 10 Hz)relative to the 100 Hz sample waveform.

Most hardware for analog-to-digital conversions uses a sample-and-holdcircuit that can charge up a capacitor for a given amount of time suchthat an average value of the waveform is determined over a specificchange in time. It will be appreciated in light of the disclosure thatthe value of the waveform over the specific change in time is not linearbut more similar to a cardinal sinusoidal (“sinc”) function; therefore,it can be shown that more emphasis can be placed on the waveform data atthe center of the sampling interval with exponential decay of thecardinal sinusoidal signal occurring from its center.

By way of the above example, the sample waveform at 100 Hz can behardware-sampled at 10 Hz and therefore each sampling point is averagedover 100 milliseconds (e.g., a signal sampled at 100 Hz can have eachpoint averaged over 10 milliseconds). In contrast to the effectivediscarding of nine out of the ten data points of the sampled waveform asdiscussed above, the present disclosure can include weighing adjacentdata. The adjacent data can refer to the sample points that werepreviously discarded and the one remaining point that was retained. Inone example, a low pass filter can average the adjacent sample datalinearly, i.e., determining the sum of every ten points and thendividing that sum by ten. In a further example, the adjacent data can beweighted with a sinc function. The process of weighting the originalwaveform with the sinc function can be referred to as an impulsefunction, or can be referred to in the time domain as a convolution.

The present disclosure can be applicable to not only digitizing awaveform signal based on a detected voltage, but can also be applicableto digitizing waveform signals based on current waveforms, vibrationwaveforms, and image processing signals including video signalrasterization. In one example, the resizing of a window on a computerscreen can be decimated, albeit in at least two directions. In thesefurther examples, it will be appreciated that undersampling by itselfcan be shown to be insufficient. To that end, oversampling or upsamplingby itself can similarly be shown to be insufficient, such thatinterpolation can be used like decimation but in lieu of onlyundersampling by itself.

It will be appreciated in light of the disclosure that interpolation inthis context can refer to first applying a low pass filter to thedigitally-sampled waveform data and then upsampling the waveform data.It will be appreciated in light of the disclosure that real-worldexamples can often require the use of non-integer factors for decimationor interpolation, or both. To that end, the present disclosure includesinterpolating and decimating sequentially in order to realize anon-integer factor rate for interpolating and decimating. In oneexample, interpolating and decimating sequentially can define applying alow-pass filter to the sample waveform, then interpolating the waveformafter the low-pass filter, and then decimating the waveform after theinterpolation. In embodiments, the vibration data can be looped topurposely emulate conventional tape recorder loops, with digitalfiltering techniques used with the effective splice to facilitate longeranalyses. It will be appreciated in light of the disclosure that theabove techniques do not preclude waveform, spectrum, and other types ofanalyses to be processed and displayed with a GUI of the user at thetime of collection. It will be appreciated in light of the disclosurethat newer systems can permit this functionality to be performed inparallel to the high-performance collection of the raw waveform data.

With respect to time of collection issues, it will be appreciated thatolder systems using the compromised approach of improving dataresolution, by collecting at different sampling rates and data lengths,do not in fact save as much time as expected. To that end, every timethe data acquisition hardware is stopped and started, latency issues canbe created, especially when there is hardware auto-scaling performed.The same can be true with respect to data retrieval of the routeinformation (i.e., test locations) that is often in a database formatand can be exceedingly slow. The storage of the raw data in bursts todisk (whether solid state or otherwise) can also be undesirably slow.

In contrast, the many embodiments include digitally streaming thewaveform data 2010, as disclosed herein, and also enjoying the benefitof needing to load the route parameter information while setting thedata acquisition hardware only once. Because the waveform data 2010 isstreamed to only one file, there is no need to open and close files, orswitch between loading and writing operations with the storage medium.It can be shown that the collection and storage of the waveform data2010, as described herein, can be shown to produce relatively moremeaningful data in significantly less time than the traditional batchdata acquisition approach. An example of this includes an electric motorabout which waveform data can be collected with a data length of 4Kpoints (i.e., 4,096) for sufficiently high resolution in order to, amongother things, distinguish electrical sideband frequencies. For fans orblowers, a reduced resolution of 1K (i.e., 1,024) can be used. Incertain instances, 1K can be the minimum waveform data lengthrequirement. The sampling rate can be 1,280 Hz and that equates to anFmax of 500 Hz. It will be appreciated in light of the disclosure thatoversampling by an industry standard factor of 2.56 can satisfy thenecessary two-times (2×) oversampling for the Nyquist Criterion withsome additional leeway that can accommodate anti-aliasingfilter-rolloff. The time to acquire this waveform data would be 1,024points at 1,280 hertz, which are 800 milliseconds.

To improve accuracy, the waveform data can be averaged. Eight averagescan be used with, for example, fifty percent overlap. This would extendthe time from 800 milliseconds to 3.6 seconds, which is equal to 800msec×8 averages×0.5 (overlap ratio)+0.5×800 msec (non-overlapped headand tail ends). After collection at Fmax=500 Hz waveform data, a highersampling rate can be used. In one example, ten times (10×) the previoussampling rate can be used and Fmax=10 kHz. By way of this example, eightaverages can be used with fifty percent (50%) overlap to collectwaveform data at this higher rate that can amount to a collection timeof 360 msec or 0.36 seconds. It will be appreciated in light of thedisclosure that it can be necessary to read the hardware collectionparameters for the higher sampling rate from the route list, as well aspermit hardware auto-scaling, or the resetting of other necessaryhardware collection parameters, or both. To that end, a few seconds oflatency can be added to accommodate the changes in sampling rate. Inother instances, introducing latency can accommodate hardwareautoscaling and changes to hardware collection parameters that can berequired when using the lower sampling rate disclosed herein. Inaddition to accommodating the change in sampling rate, additional timeis needed for reading the route point information from the database(i.e., where to monitor and where to monitor next), displaying the routeinformation, and processing the waveform data. Moreover, display of thewaveform data and/or associated spectra can also consume significanttime. In light of the above, 15 seconds to 20 seconds can elapse whileobtaining waveform data at each measurement point.

In further examples, additional sampling rates can be added but this canmake the total amount time for the vibration survey even longer becausetime adds up from changeover time from one sampling rate to another andfrom the time to obtain additional data at different sampling rate. Inone example, a lower sampling rate is used, such as a sampling rate of128 Hz where Fmax=50 Hz. By way of this example, the vibration surveywould, therefore, require an additional 36 seconds for the first set ofaveraged data at this sampling rate, in addition to others mentionedabove, and consequently the total time spent at each measurement pointincreases even more dramatically. Further embodiments include usingsimilar digital streaming of gap free waveform data as disclosed hereinfor use with wind turbines and other machines that can have relativelyslow speed rotating or oscillating systems. In many examples, thewaveform data collected can include long samples of data at a relativelyhigh-sampling rate. In one example, the sampling rate can be 100 kHz andthe sampling duration can be for two minutes on all of the channelsbeing recorded. In many examples, one channel can be for the single-axisreference sensor and three more data channels can be for the tri-axialthree channel sensor. It will be appreciated in light of the disclosurethat the long data length can be shown to facilitate detection ofextremely low frequency phenomena. The long data length can also beshown to accommodate the inherent speed variability in wind turbineoperations. Additionally, the long data length can further be shown toprovide the opportunity for using numerous averages such as thosediscussed herein, to achieve very high spectral resolution, and to makefeasible tape loops for certain spectral analyses. Many multipleadvanced analytical techniques can now become available because suchtechniques can use the available long uninterrupted length of waveformdata in accordance with the present disclosure.

It will also be appreciated in light of the disclosure that thesimultaneous collection of waveform data from multiple channels canfacilitate performing transfer functions between multiple channels.Moreover, the simultaneous collection of waveform data from multiplechannels facilitates establishing phase relationships across the machineso that more sophisticated correlations can be utilized by relying onthe fact that the waveforms from each of the channels are collectedsimultaneously. In other examples, more channels in the data collectioncan be used to reduce the time it takes to complete the overallvibration survey by allowing for simultaneous acquisition of waveformdata from multiple sensors that otherwise would have to be acquired, ina subsequent fashion, moving sensor to sensor in the vibration survey.

The present disclosure includes the use of at least one of thesingle-axis reference probe on one of the channels to allow foracquisition of relative phase comparisons between channels. Thereference probe can be an accelerometer or other type of transducer thatis not moved and, therefore, fixed at an unchanging location during thevibration survey of one machine. Multiple reference probes can each bedeployed as at suitable locations fixed in place (i.e., at unchanginglocations) throughout the acquisition of vibration data during thevibration survey. In certain examples, up to seven reference probes canbe deployed depending on the capacity of the data collection module 2160or the like. Using transfer functions or similar techniques, therelative phases of all channels may be compared with one another at allselected frequencies. By keeping the one or more reference probes fixedat their unchanging locations while moving or monitoring the othertri-axial vibration sensors, it can be shown that the entire machine canbe mapped with regard to amplitude and relative phase. This can be shownto be true even when there are more measurement points than channels ofdata collection. With this information, an operating deflection shapecan be created that can show dynamic movements of the machine in 3D,which can provide an invaluable diagnostic tool. In embodiments, the oneor more reference probes can provide relative phase, rather thanabsolute phase. It will be appreciated in light of the disclosure thatrelative phase may not be as valuable absolute phase for some purposes,but the relative phase information can still be shown to be very useful.

In embodiments, the sampling rates used during the vibration survey canbe digitally synchronized to predetermined operational frequencies thatcan relate to pertinent parameters of the machine such as rotating oroscillating speed. Doing this, permits extracting even more informationusing synchronized averaging techniques. It will be appreciated in lightof the disclosure that this can be done without the use of a key phasoror a reference pulse from a rotating shaft, which is usually notavailable for route collected data. As such, non-synchronous signals canbe removed from a complex signal without the need to deploy synchronousaveraging using the key phasor. This can be shown to be very powerfulwhen analyzing a particular pinion in a gearbox or generally applied toany component within a complicated mechanical mechanism. In manyinstances, the key phasor or the reference pulse is rarely availablewith route collected data, but the techniques disclosed herein canovercome this absence. In embodiments, there can be multiple shaftsrunning at different speeds within the machine being analyzed. Incertain instances, there can be a single-axis reference probe for eachshaft. In other instances, it is possible to relate the phase of oneshaft to another shaft using only one single-axis reference probe on oneshaft at its unchanging location. In embodiments, variable speedequipment can be more readily analyzed with relatively longer durationof data relative to single speed equipment. The vibration survey can beconducted at several machine speeds within the same contiguous set ofvibration data using the same techniques disclosed herein. Thesetechniques can also permit the study of the change of the relationshipbetween vibration and the change of the rate of speed that was notavailable before.

In embodiments, there are numerous analytical techniques that can emergefrom because raw waveform data can be captured in a gap-free digitalformat as disclosed herein. The gap-free digital format can facilitatemany paths to analyze the waveform data in many ways after the fact toidentify specific problems. The vibration data collected in accordancewith the techniques disclosed herein can provide the analysis oftransient, semi-periodic and very low frequency phenomena. The waveformdata acquired in accordance with the present disclosure can containrelatively longer streams of raw gap-free waveform data that can beconveniently played back as needed, and on which many and variedsophisticated analytical techniques can be performed. A large number ofsuch techniques can provide for various forms of filtering to extractlow amplitude modulations from transient impact data that can beincluded in the relatively longer stream of raw gap-free waveform data.It will be appreciated in light of the disclosure that in past datacollection practices, these types of phenomena were typically lost bythe averaging process of the spectral processing algorithms because thegoal of the previous data acquisition module was purely periodicsignals; or these phenomena were lost to file size reductionmethodologies due to the fact that much of the content from an originalraw signal was typically discarded knowing it would not be used.

In embodiments, there is a method of monitoring vibration of a machinehaving at least one shaft supported by a set of bearings. The methodincludes monitoring a first data channel assigned to a single-axissensor at an unchanging location associated with the machine. The methodalso includes monitoring a second, third, and fourth data channelassigned to a three-axis sensor. The method further includes recordinggap-free digital waveform data simultaneously from all of the datachannels while the machine is in operation; and determining a change inrelative phase based on the digital waveform data. The method alsoincludes the tri-axial sensor being located at a plurality of positionsassociated with the machine while obtaining the digital waveform. Inembodiments, the second, third, and fourth channels are assignedtogether to a sequence of tri-axial sensors each located at differentpositions associated with the machine. In embodiments, the data isreceived from all of the sensors on all of their channelssimultaneously.

The method also includes determining an operating deflection shape basedon the change in relative phase information and the waveform data. Inembodiments, the unchanging location of the reference sensor is aposition associated with a shaft of the machine. In embodiments, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings in the machine. In embodiments, the unchanging location is aposition associated with a shaft of the machine and, wherein, thetri-axial sensors in the sequence of the tri-axial sensors are eachlocated at different positions and are each associated with differentbearings that support the shaft in the machine. The various embodimentsinclude methods of sequentially monitoring vibration or similar processparameters and signals of a rotating or oscillating machine or analogousprocess machinery from a number of channels simultaneously, which can beknown as an ensemble. In various examples, the ensemble can include oneto eight channels. In further examples, an ensemble can represent alogical measurement grouping on the equipment being monitored whetherthose measurement locations are temporary for measurement, supplied bythe original equipment manufacturer, retrofit at a later date, or one ormore combinations thereof.

In one example, an ensemble can monitor bearing vibration in a singledirection. In a further example, an ensemble can monitor three differentdirections (e.g., orthogonal directions) using a tri-axial sensor. Inyet further examples, an ensemble can monitor four or more channelswhere the first channel can monitor a single-axis vibration sensor, andthe second, the third, and the fourth channels can monitor each of thethree directions of the tri-axial sensor. In other examples, theensemble can be fixed to a group of adjacent bearings on the same pieceof equipment or an associated shaft. The various embodiments providemethods that include strategies for collecting waveform data fromvarious ensembles deployed in vibration studies or the like in arelatively more efficient manner. The methods also includesimultaneously monitoring of a reference channel assigned to anunchanging reference location associated with the ensemble monitoringthe machine. The cooperation with the reference channel can be shown tosupport a more complete correlation of the collected waveforms from theensembles. The reference sensor on the reference channel can be asingle-axis vibration sensor, or a phase reference sensor that can betriggered by a reference location on a rotating shaft or the like. Asdisclosed herein, the methods can further include recording gap-freedigital waveform data simultaneously from all of the channels of eachensemble at a relatively high rate of sampling so as to include allfrequencies deemed necessary for the proper analysis of the machinerybeing monitored while it is in operation. The data from the ensemblescan be streamed gap-free to a storage medium for subsequent processingthat can be connected to a cloud network facility, a local data link,Bluetooth™ connectivity, cellular data connectivity, or the like.

In embodiments, the methods disclosed herein include strategies forcollecting data from the various ensembles including digital signalprocessing techniques that can be subsequently applied to data from theensembles to emphasize or better isolate specific frequencies orwaveform phenomena. This can be in contrast with current methods thatcollect multiple sets of data at different sampling rates, or withdifferent hardware filtering configurations including integration, thatprovide relatively less post-processing flexibility because of thecommitment to these same (known as a priori hardware configurations).These same hardware configurations can also be shown to increase time ofthe vibration survey due to the latency delays associated withconfiguring the hardware for each independent test. In embodiments, themethods for collecting data from various ensembles include data markertechnology that can be used for classifying sections of streamed data ashomogenous and belonging to a specific ensemble. In one example, aclassification can be defined as operating speed. In doing so, amultitude of ensembles can be created from what conventional systemswould collect as only one. The many embodiments include post-processinganalytic techniques for comparing the relative phases of all thefrequencies of interest not only between each channel of the collectedensemble but also between all of the channels of all of the ensemblesbeing monitored, when applicable.

With reference to FIG. 12, the many embodiments include a first machine2400 having rotating or oscillating components 2410, or both, eachsupported by a set of bearings 2420 including a bearing pack 2422, abearing pack 2424, a bearing pack 2426, and more as needed. The firstmachine 2400 can be monitored by a first sensor ensemble 2450. The firstensemble 2450 can be configured to receive signals from sensorsoriginally installed (or added later) on the first machine 2400. Thesensors on the machine 2400 can include single-axis sensors 2460, suchas a single-axis sensor 2462, a single-axis sensor 2464, and more asneeded. In many examples, the single-axis sensors 2460 can be positionedin the machine 2400 at locations that allow for the sensing of one ofthe rotating or oscillating components 2410 of the machine 2400.

The machine 2400 can also have tri-axial (e.g., orthogonal axes) sensors2480, such as a tri-axial sensor 2482, a tri-axial sensor 2484, and moreas needed. In many examples, the tri-axial sensors 2480 can bepositioned in the machine 2400 at locations that allow for the sensingof one of each of the bearing packs in the sets of bearings 2420 that isassociated with the rotating or oscillating components of the machine2400. The machine 2400 can also have temperature sensors 2500, such as atemperature sensor 2502, a temperature sensor 2504, and more as needed.The machine 2400 can also have a tachometer sensor 2510 or more asneeded that each detail the RPMs of one of its rotating components. Byway of the above example, the first sensor ensemble 2450 can survey theabove sensors associated with the first machine 2400. To that end, thefirst ensemble 2450 can be configured to receive eight channels. Inother examples, the first sensor ensemble 2450 can be configured to havemore than eight channels, or less than eight channels as needed. In thisexample, the eight channels include two channels that can each monitor asingle-axis reference sensor signal and three channels that can monitora tri-axial sensor signal. The remaining three channels can monitor twotemperature signals and a signal from a tachometer. In one example, thefirst ensemble 2450 can monitor the single-axis sensor 2462, thesingle-axis sensor 2464, the tri-axial sensor 2482, the temperaturesensor 2502, the temperature sensor 2504, and the tachometer sensor 2510in accordance with the present disclosure. During a vibration survey onthe machine 2400, the first ensemble 2450 can first monitor thetri-axial sensor 2482 and then move next to the tri-axial sensor 2484.

After monitoring the tri-axial sensor 2484, the first ensemble 2450 canmonitor additional tri-axial sensors on the machine 2400 as needed andthat are part of the predetermined route list associated with thevibration survey of the machine 2400, in accordance with the presentdisclosure. During this vibration survey, the first ensemble 2450 cancontinually monitor the single-axis sensor 2462, the single-axis sensor2464, the two temperature sensors 2502, 2504, and the tachometer sensor2510 while the first ensemble 2450 can serially monitor the multipletri-axial sensors 2480 in the pre-determined route plan for thisvibration survey.

With reference to FIG. 12, the many embodiments include a second machine2600 having rotating or oscillating components 2610, or both, eachsupported by a set of bearings 2620 including a bearing pack 2622, abearing pack 2624, a bearing pack 2626, and more as needed. The secondmachine 2600 can be monitored by a second sensor ensemble 2650. Thesecond ensemble 2650 can be configured to receive signals from sensorsoriginally installed (or added later) on the second machine 2600. Thesensors on the machine 2600 can include single-axis sensors 2660, suchas a single-axis sensor 2662, a single-axis sensor 2664, and more asneeded. In many examples, the single-axis sensors 2660 can be positionedin the machine 2600 at locations that allow for the sensing of one ofthe rotating or oscillating components 2610 of the machine 2600.

The machine 2600 can also have tri-axial (e.g., orthogonal axes) sensors2680, such as a tri-axial sensor 2682, a tri-axial sensor 2684, atri-axial sensor 2686, a tri-axial sensor 2688, and more as needed. Inmany examples, the tri-axial sensors 2680 can be positioned in themachine 2600 at locations that allow for the sensing of one of each ofthe bearing packs in the sets of bearings 2620 that is associated withthe rotating or oscillating components of the machine 2600. The machine2600 can also have temperature sensors 2700, such as a temperaturesensor 2702, a temperature sensor 2704, and more as needed. The machine2600 can also have a tachometer sensor 2710 or more as needed that eachdetail the RPMs of one of its rotating components.

By way of the above example, the second sensor ensemble 2650 can surveythe above sensors associated with the second machine 2600. To that end,the second ensemble 2650 can be configured to receive eight channels. Inother examples, the second sensor ensemble 2650 can be configured tohave more than eight channels or less than eight channels as needed. Inthis example, the eight channels include one channel that can monitor asingle-axis reference sensor signal and six channels that can monitortwo tri-axial sensor signals. The remaining channel can monitor atemperature signal. In one example, the second ensemble 2650 can monitorthe single-axis sensor 2662, the tri-axial sensor 2682, the tri-axialsensor 2684, and the temperature sensor 2702. During a vibration surveyon the machine 2600 in accordance with the present disclosure, thesecond ensemble 2650 can first monitor the tri-axial sensor 2682simultaneously with the tri-axial sensor 2684 and then move onto thetri-axial sensor 2686 simultaneously with the tri-axial sensor 2688.

After monitoring the tri-axial sensors 2680, the second ensemble 2650can monitor additional tri-axial sensors (in simultaneous pairs) on themachine 2600 as needed and that are part of the predetermined route listassociated with the vibration survey of the machine 2600 in accordancewith the present disclosure. During this vibration survey, the secondensemble 2650 can continually monitor the single-axis sensor 2662 at itsunchanging location and the temperature sensor 2702 while the secondensemble 2650 can serially monitor the multiple tri-axial sensors in thepre-determined route plan for this vibration survey.

With continuing reference to FIG. 12, the many embodiments include athird machine 2800 having rotating or oscillating components 2810, orboth, each supported by a set of bearings 2820 including a bearing pack2822, a bearing pack 2824, a bearing pack 2826, and more as needed. Thethird machine 2800 can be monitored by a third sensor ensemble 2850. Thethird ensemble 2850 can be configured with a single-axis sensor 2860,and two tri-axial (e.g., orthogonal axes) sensors 2880, 2882. In manyexamples, the single-axis sensor 2860 can be secured by the user on themachine 2800 at a location that allows for the sensing of one of therotating or oscillating components of the machine 2800. The tri-axialsensors 2880, 2882 can also be located on the machine 2800 by the userat locations that allow for the sensing of one of each of the bearingsin the sets of bearings that each associated with the rotating oroscillating components of the machine 2800. The third ensemble 2850 canalso include a temperature sensor 2900. The third ensemble 2850 and itssensors can be moved to other machines unlike the first and secondensembles 2450, 2650.

The many embodiments also include a fourth machine 2950 having rotatingor oscillating components 2960, or both, each supported by a set ofbearings 2970 including a bearing pack 2972, a bearing pack 2974, abearing pack 2976, and more as needed. The fourth machine 2950 can bealso monitored by the third sensor ensemble 2850 when the user moves itto the fourth machine 2950. The many embodiments also include a fifthmachine 3000 having rotating or oscillating components 3010, or both.The fifth machine 3000 may not be explicitly monitored by any sensor orany sensor ensembles in operation but it can create vibrations or otherimpulse energy of sufficient magnitude to be recorded in the dataassociated with any one of the machines 2400, 2600, 2800, 2950 under avibration survey.

The many embodiments include monitoring the first sensor ensemble 2450on the first machine 2400 through the predetermined route as disclosedherein. The many embodiments also include monitoring the second sensorensemble 2650 on the second machine 2600 through the predeterminedroute. The locations of machine 2400 being close to machine 2600 can beincluded in the contextual metadata of both vibration surveys. The thirdensemble 2850 can be moved between machine 2800, machine 2950, and othersuitable machines. The machine 3000 has no sensors onboard asconfigured, but could be monitored as needed by the third sensorensemble 2850. The machine 3000 and its operational characteristics canbe recorded in the metadata in relation to the vibration surveys on theother machines to note its contribution due to its proximity.

The many embodiments include hybrid database adaptation for harmonizingrelational metadata and streaming raw data formats. Unlike older systemsthat utilized traditional database structure for associating nameplateand operational parameters (sometimes deemed metadata) with individualdata measurements that are discrete and relatively simple, it will beappreciated in light of the disclosure that more modern systems cancollect relatively larger quantities of raw streaming data with highersampling rates and greater resolutions. At the same time, it will alsobe appreciated in light of the disclosure that the network of metadatawith which to link and obtain this raw data or correlate with this rawdata, or both, is expanding at ever-increasing rates.

In one example, a single overall vibration level can be collected aspart of a route or prescribed list of measurement points. This datacollected can then be associated with database measurement locationinformation for a point located on a surface of a bearing housing on aspecific piece of the machine adjacent to a coupling in a verticaldirection. Machinery analysis parameters relevant to the proper analysiscan be associated with the point located on the surface. Examples ofmachinery analysis parameters relevant to the proper analysis caninclude a running speed of a shaft passing through the measurement pointon the surface. Further examples of machinery analysis parametersrelevant to the proper analysis can include one of, or a combination of:running speeds of all component shafts for that piece of equipmentand/or machine, bearing types being analyzed such as sleeve or rollingelement bearings, the number of gear teeth on gears should there be agearbox, the number of poles in a motor, slip and line frequency of amotor, roller bearing element dimensions, number of fan blades, or thelike. Examples of machinery analysis parameters relevant to the properanalysis can further include machine operating conditions such as theload on the machines and whether load is expressed in percentage,wattage, air flow, head pressure, horsepower, and the like. Furtherexamples of machinery analysis parameters include information relevantto adjacent machines that might influence the data obtained during thevibration study.

It will be appreciated in light of the disclosure that the vast array ofequipment and machinery types can support many differentclassifications, each of which can be analyzed in distinctly differentways. For example, some machines, like screw compressors and hammermills, can be shown to run much noisier and can be expected to vibratesignificantly more than other machines. Machines known to vibrate moresignificantly can be shown to require a change in vibration levels thatcan be considered acceptable relative to quieter machines.

The present disclosure further includes hierarchical relationships foundin the vibrational data collected that can be used to support properanalysis of the data. One example of the hierarchical data includes theinterconnection of mechanical componentry such as a bearing beingmeasured in a vibration survey and the relationship between thatbearing, including how that bearing connects to a particular shaft onwhich is mounted a specific pinion within a particular gearbox, and therelationship between the shaft, the pinion, and the gearbox. Thehierarchical data can further include in what particular spot within amachinery gear train that the bearing being monitored is locatedrelative to other components in the machine. The hierarchical data canalso detail whether the bearing being measured in a machine is in closeproximity to another machine whose vibrations may affect what is beingmeasured in the machine that is the subject of the vibration study.

The analysis of the vibration data from the bearing or other componentsrelated to one another in the hierarchical data can use table lookups,searches for correlations between frequency patterns derived from theraw data, and specific frequencies from the metadata of the machine. Insome embodiments, the above can be stored in and retrieved from arelational database. In embodiments, National Instrument's TechnicalData Management Solution (TDMS) file format can be used. The TDMS fileformat can be optimized for streaming various types of measurement data(i.e., binary digital samples of waveforms), as well as also being ableto handle hierarchical metadata.

The many embodiments include a hybrid relational metadata-binary storageapproach (HRM-BSA). The HRM-BSA can include a structured query language(SQL) based relational database engine. The structured query languagebased relational database engine can also include a raw data engine thatcan be optimized for throughput and storage density for data that isflat and relatively structureless. It will be appreciated in light ofthe disclosure that benefits can be shown in the cooperation between thehierarchical metadata and the SQL relational database engine. In oneexample, marker technologies and pointer sign-posts can be used to makecorrelations between the raw database engine and the SQL relationaldatabase engine. Three examples of correlations between the raw databaseengine and the SQL relational database engine linkages include: (1)pointers from the SQL database to the raw data; (2) pointers from theancillary metadata tables or similar grouping of the raw data to the SQLdatabase; and (3) independent storage tables outside the domain ofeither the SQL database or raw data technologies.

With reference to FIG. 13, the present disclosure can include pointersfor Group 1 and Group 2 that can include associated filenames, pathinformation, table names, database key fields as employed with existingSQL database technologies that can be used to associate a specificdatabase segments or locations, asset properties to specific measurementraw data streams, records with associated time/date stamps, orassociated metadata such as operating parameters, panel conditions, andthe like. By way of this example, a plant 3200 can include machine one3202, machine two 3204, and many others in the plant 3200. The machineone 3202 can include a gearbox 3210, a motor 3212, and other elements.The machine two 3204 can include a motor 3220, and other elements. Manywaveforms 3230 including waveform 3240, waveform 3242, waveform 3244,and additional waveforms as needed can be acquired from the machines3202, 3204 in the plant 3200. The waveforms 3230 can be associated withthe local marker linking tables 3300 and the linking raw data tables3400. The machines 3202, 3204 and their elements can be associated withlinking tables having relational databases 3500. The linking raw datatables 3400 and the linking tables having relational databases 3500 canbe associated with the linking tables with optional independent storagetables 3600.

The present disclosure can include markers that can be applied to a timemark or a sample length within the raw waveform data. The markersgenerally fall into two categories: preset or dynamic. The presetmarkers can correlate to preset or existing operating conditions (e.g.,load, head pressure, air flow cubic feet per minute, ambienttemperature, RPMs, and the like.). These preset markers can be fed intothe data acquisition system directly. In certain instances, the presetmarkers can be collected on data channels in parallel with the waveformdata (e.g., waveforms for vibration, current, voltage, etc.).Alternatively, the values for the preset markers can be enteredmanually.

For dynamic markers such as trending data, it can be important tocompare similar data like comparing vibration amplitudes and patternswith a repeatable set of operating parameters. One example of thepresent disclosure includes one of the parallel channel inputs being akey phasor trigger pulse from an operating shaft that can provide RPMinformation at the instantaneous time of collection. In this example ofdynamic markers, sections of collected waveform data can be marked withappropriate speeds or speed ranges.

The present disclosure can also include dynamic markers that cancorrelate to data that can be derived from post processing and analyticsperformed on the sample waveform. In further embodiments, the dynamicmarkers can also correlate to post-collection derived parametersincluding RPMs, as well as other operationally derived metrics such asalarm conditions like a maximum RPM. In certain examples, many modernpieces of equipment that are candidates for a vibration survey with theportable data collection systems described herein do not includetachometer information. This can be true because it is not alwayspractical or cost-justifiable to add a tachometer even though themeasurement of RPM can be of primary importance for the vibration surveyand analysis. It will be appreciated that for fixed speed machineryobtaining an accurate RPM measurement can be less important especiallywhen the approximate speed of the machine can be ascertainedbefore-hand; however, variable-speed drives are becoming more and moreprevalent. It will also be appreciated in light of the disclosure thatvarious signal processing techniques can permit the derivation of RPMfrom the raw data without the need for a dedicated tachometer signal.

In many embodiments, the RPM information can be used to mark segments ofthe raw waveform data over its collection history. Further embodimentsinclude techniques for collecting instrument data following a prescribedroute of a vibration study. The dynamic markers can enable analysis andtrending software to utilize multiple segments of the collectioninterval indicated by the markers (e.g., two minutes) as multiplehistorical collection ensembles, rather than just one as done inprevious systems where route collection systems would historically storedata for only one RPM setting. This could, in turn, be extended to anyother operational parameter such as load setting, ambient temperature,and the like, as previously described. The dynamic markers, however,that can be placed in a type of index file pointing to the raw datastream can classify portions of the stream in homogenous entities thatcan be more readily compared to previously collected portions of the rawdata stream.

The many embodiments include the hybrid relational metadata-binarystorage approach that can use the best of pre-existing technologies forboth relational and raw data streams. In embodiments, the hybridrelational metadata-binary storage approach can marry them together witha variety of marker linkages. The marker linkages can permit rapidsearches through the relational metadata and can allow for moreefficient analyses of the raw data using conventional SQL techniqueswith pre-existing technology. This can be shown to permit utilization ofmany of the capabilities, linkages, compatibilities, and extensions thatconventional database technologies do not provide.

The marker linkages can also permit rapid and efficient storage of theraw data using conventional binary storage and data compressiontechniques. This can be shown to permit utilization of many of thecapabilities, linkages, compatibilities, and extensions thatconventional raw data technologies provide such as TDMS (NationalInstruments), UFF (Universal File Format such as UFF58), and the like.The marker linkages can further permit using the marker technology linkswhere a vastly richer set of data from the ensembles can be amassed inthe same collection time as more conventional systems. The richer set ofdata from the ensembles can store data snapshots associated withpredetermined collection criterion and the proposed system can derivemultiple snapshots from the collected data streams utilizing the markertechnology. In doing so, it can be shown that a relatively richeranalysis of the collected data can be achieved. One such benefit caninclude more trending points of vibration at a specific frequency ororder of running speed versus RPM, load, operating temperature, flowrates, and the like, which can be collected for a similar time relativeto what is spent collecting data with a conventional system.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines, elements of the machines and the environment of the machinesincluding heavy duty machines deployed at a local job site or atdistributed job sites under common control. The heavy-duty machines mayinclude earthmoving equipment, heavy duty on-road industrial vehicles,heavy duty off-road industrial vehicles, industrial machines deployed invarious settings such as turbines, turbomachinery, generators, pumps,pulley systems, manifold and valve systems, and the like. Inembodiments, heavy industrial machinery may also include earth-movingequipment, earth-compacting equipment, hauling equipment, hoistingequipment, conveying equipment, aggregate production equipment,equipment used in concrete construction, and piledriving equipment. Inexamples, earth moving equipment may include excavators, backhoes,loaders, bulldozers, skid steer loaders, trenchers, motor graders, motorscrapers, crawler loaders, and wheeled loading shovels. In examples,construction vehicles may include dumpers, tankers, tippers, andtrailers. In examples, material handling equipment may include cranes,conveyors, forklift, and hoists. In examples, construction equipment mayinclude tunnel and handling equipment, road rollers, concrete mixers,hot mix plants, road making machines (compactors), stone crashers,pavers, slurry seal machines, spraying and plastering machines, andheavy-duty pumps. Further examples of heavy industrial equipment mayinclude different systems such as implement traction, structure, powertrain, control, and information. Heavy industrial equipment may includemany different powertrains and combinations thereof to provide power forlocomotion and to also provide power to accessories and onboardfunctionality. In each of these examples, the platform 100 may deploythe local data collection system 102 into the environment 104 in whichthese machines, motors, pumps, and the like, operate and directlyconnected integrated into each of the machines, motors, pumps, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals frommachines in operation and machines in being constructed such as turbineand generator sets like Siemens™ SGT6-5000F™ gas turbine, an SST-900™steam turbine, an SGen6-1000A™ generator, and an SGen6-100A™ generator,and the like. In embodiments, the local data collection system 102 maybe deployed to monitor steam turbines as they rotate in the currentscaused by hot water vapor that may be directed through the turbine butotherwise generated from a different source such as from gas-firedburners, nuclear cores, molten salt loops and the like. In thesesystems, the local data collection system 102 may monitor the turbinesand the water or other fluids in a closed loop cycle in which watercondenses and is then heated until it evaporates again. The local datacollection system 102 may monitor the steam turbines separately from thefuel source deployed to heat the water to steam. In examples, workingtemperatures of steam turbines may be between 500 and 650° C. In manyembodiments, an array of steam turbines may be arranged and configuredfor high, medium, and low pressure, so they may optimally convert therespective steam pressure into rotational movement.

The local data collection system 102 may also be deployed in a gasturbines arrangement and therefore not only monitor the turbine inoperation but also monitor the hot combustion gases feed into theturbine that may be in excess of 1,500° C. Because these gases are muchhotter than those in steam turbines, the blades may be cooled with airthat may flow out of small openings to create a protective film orboundary layer between the exhaust gases and the blades. Thistemperature profile may be monitored by the local data collection system102. Gas turbine engines, unlike typical steam turbines, include acompressor, a combustion chamber, and a turbine all of which arejournaled for rotation with a rotating shaft. The construction andoperation of each of these components may be monitored by the local datacollection system 102.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from waterturbines serving as rotary engines that may harvest energy from movingwater and are used for electric power generation. The type of waterturbine or hydro-power selected for a project may be based on the heightof standing water, often referred to as head, and the flow (or volume ofwater) at the site. In this example, a generator may be placed at thetop of a shaft that connects to the water turbine. As the turbinecatches the naturally moving water in its blade and rotates, the turbinesends rotational power to the generator to generate electrical energy.In doing so, the platform 100 may monitor signals from the generators,the turbines, the local water system, flow controls such as dam windowsand sluices. Moreover, the platform 100 may monitor local conditions onthe electric grid including load, predicted demand, frequency response,and the like, and include such information in the monitoring and controldeployed by platform 100 in these hydroelectric settings.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromenergy production environments, such as thermal, nuclear, geothermal,chemical, biomass, carbon-based fuels, hybrid-renewable energy plants,and the like. Many of these plants may use multiple forms of energyharvesting equipment like wind turbines, hydro turbines, and steamturbines powered by heat from nuclear, gas-fired, solar, and molten saltheat sources. In embodiments, elements in such systems may includetransmission lines, heat exchangers, desulphurization scrubbers, pumps,coolers, recuperators, chillers, and the like. In embodiments, certainimplementations of turbomachinery, turbines, scroll compressors, and thelike may be configured in arrayed control so as to monitor largefacilities creating electricity for consumption, providingrefrigeration, creating steam for local manufacture and heating, and thelike, and that arrayed control platforms may be provided by the providerof the industrial equipment such as Honeywell and their Experion™ PKSplatform. In embodiments, the platform 100 may specifically communicatewith and integrate the local manufacturer-specific controls and mayallow equipment from one manufacturer to communicate with otherequipment. Moreover, the platform 100 provides allows for the local datacollection system 102 to collect information across systems from manydifferent manufacturers. In embodiments, the platform 100 may includethe local data collection system 102 deployed in the environment 104 tomonitor signals from marine industrial equipment, marine diesel engines,shipbuilding, oil and gas plants, refineries, petrochemical plant,ballast water treatment solutions, marine pumps and turbines, and thelike.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from heavyindustrial equipment and processes including monitoring one or moresensors. By way of this example, sensors may be devices that may be usedto detect or respond to some type of input from a physical environment,such as an electrical, heat, or optical signal. In embodiments, thelocal data collection system 102 may include multiple sensors such as,without limitation, a temperature sensor, a pressure sensor, a torquesensor, a flow sensor, a heat sensor, a smoke sensor, an arc sensor, aradiation sensor, a position sensor, an acceleration sensor, a strainsensor, a pressure cycle sensor, a pressure sensor, an air temperaturesensor, and the like. The torque sensor may encompass a magnetic twistangle sensor. In one example, the torque and speed sensors in the localdata collection system 102 may be similar to those discussed in U.S.Pat. No. 8,352,149 to Meachem, issued 8 Jan. 2013 and herebyincorporated by reference as if fully set forth herein. In embodiments,one or more sensors may be provided such as a tactile sensor, abiosensor, a chemical sensor, an image sensor, a humidity sensor, aninertial sensor, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors that may provide signals for fault detection including excessivevibration, incorrect material, incorrect material properties, truenessto the proper size, trueness to the proper shape, proper weight,trueness to balance. Additional fault sensors include those forinventory control and for inspections such as to confirm that parts arepackaged to plan, parts are to tolerance in a plan, occurrence ofpackaging damage or stress, and sensors that may indicate the occurrenceof shock or damage in transit. Additional fault sensors may includedetection of the lack of lubrication, over lubrication, the need forcleaning of the sensor detection window, the need for maintenance due tolow lubrication, the need for maintenance due to blocking or reducedflow in a lubrication region, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 that includes aircraftoperations and manufacture including monitoring signals from sensors forspecialized applications such as sensors used in an aircraft's Attitudeand Heading Reference System (AHRS), such as gyroscopes, accelerometers,and magnetometers. In embodiments, the platform 100 may include thelocal data collection system 102 deployed in the environment 104 tomonitor signals from image sensors such as semiconductor charge coupleddevices (CCDs), active pixel sensors, in complementarymetal-oxide-semiconductor (CMOS) or N-type metal-oxide-semiconductor(NMOS, Live MOS) technologies. In embodiments, the platform 100 mayinclude the local data collection system 102 deployed in the environment104 to monitor signals from sensors such as an infra-red (IR) sensor, anultraviolet (UV) sensor, a touch sensor, a proximity sensor, and thelike. In embodiments, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom sensors configured for optical character recognition (OCR), readingbarcodes, detecting surface acoustic waves, detecting transponders,communicating with home automation systems, medical diagnostics, healthmonitoring, and the like.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromsensors such as a Micro-Electro-Mechanical Systems (MEMS) sensor, suchas STMicroelectronics™ LSM303AH smart MEMS sensor, which may include anultra-low-power high-performance system-in-package featuring a 3Ddigital linear acceleration sensor and a 3D digital magnetic sensor.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromadditional large machines such as turbines, windmills, industrialvehicles, robots, and the like. These large mechanical machines includemultiple components and elements providing multiple subsystems on eachmachine. To that end, the platform 100 may include the local datacollection system 102 deployed in the environment 104 to monitor signalsfrom individual elements such as axles, bearings, belts, buckets, gears,shafts, gear boxes, cams, carriages, camshafts, clutches, brakes, drums,dynamos, feeds, flywheels, gaskets, pumps, jaws, robotic arms, seals,sockets, sleeves, valves, wheels, actuators, motors, servomotor, and thelike. Many of the machines and their elements may include servomotors.The local data collection system 102 may monitor the motor, the rotaryencoder, and the potentiometer of the servomechanism to providethree-dimensional detail of position, placement, and progress ofindustrial processes.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals from geardrives, powertrains, transfer cases, multispeed axles, transmissions,direct drives, chain drives, belt-drives, shaft-drives, magnetic drives,and similar meshing mechanical drives. In embodiments, the platform 100may include the local data collection system 102 deployed in theenvironment 104 to monitor signals from fault conditions of industrialmachines that may include overheating, noise, grinding gears, lockedgears, excessive vibration, wobbling, under-inflation, over-inflation,and the like. Operation faults, maintenance indicators, and interactionsfrom other machines may cause maintenance or operational issues mayoccur during operation, during installation, and during maintenance. Thefaults may occur in the mechanisms of the industrial machines but mayalso occur in infrastructure that supports the machine such as itswiring and local installation platforms. In embodiments, the largeindustrial machines may face different types of fault conditions such asoverheating, noise, grinding gears, excessive vibration of machineparts, fan vibration problems, problems with large industrial machinesrotating parts.

In embodiments, the platform 100 may include the local data collectionsystem 102 deployed in the environment 104 to monitor signals fromindustrial machinery including failures that may be caused by prematurebearing failure that may occur due to contamination or loss of bearinglubricant. In another example, a mechanical defect such as misalignmentof bearings may occur. Many factors may contribute to the failure suchas metal fatigue, therefore, the local data collection system 102 maymonitor cycles and local stresses. By way of this example, the platform100 may monitor the incorrect operation of machine parts, lack ofmaintenance and servicing of parts, corrosion of vital machine parts,such as couplings or gearboxes, misalignment of machine parts, and thelike. Though the fault occurrences cannot be completely stopped, manyindustrial breakdowns may be mitigated to reduce operational andfinancial losses. The platform 100 provides real-time monitoring andpredictive maintenance in many industrial environments wherein it hasbeen shown to present a cost-savings over regularly-scheduledmaintenance processes that replace parts according to a rigid expirationof time and not actual load and wear and tear on the element or machine.To that end, the platform 10 may provide reminders of, or perform some,preventive measures such as adhering to operating manual and modeinstructions for machines, proper lubrication, and maintenance ofmachine parts, minimizing or eliminating overrun of machines beyondtheir defined capacities, replacement of worn but still functional partsas needed, properly training the personnel for machine use, and thelike.

In embodiments, methods and systems of data collection in an industrialenvironment may include a plurality of industrial condition sensing andacquisition modules that may include at least one programmable logiccomponent per module that may control a portion of the sensing andacquisition functionality of its module. The programmable logiccomponents on each of the modules may be interconnected by a dedicatedlogic bus that may include data and control channels. The dedicatedlogic bus may extend logically and/or physically to other programmablelogic components on other sensing and acquisition modules. Inembodiments, the programmable logic components may be programmed via thededicated interconnection bus, via a dedicated programming portion ofthe dedicated interconnection bus, via a program that is passed betweenprogrammable logic components, sensing and acquisition modules, or wholesystems. A programmable logic component for use in an industrialenvironment data sensing and acquisition system may be a ComplexProgrammable Logic Device, an Application-Specific Integrated Circuit,microcontrollers, and combinations thereof.

A programmable logic component in an industrial data collectionenvironment may perform control functions associated with datacollection. Control examples include power control of analog channels,sensors, analog receivers, analog switches, portions of logic modules(e.g., a logic board, system, and the like) on which the programmablelogic component is disposed, self-power-up/down, self-sleep/wake up, andthe like. Control functions, such as these and others, may be performedin coordination with control and operational functions of otherprogrammable logic components, such as other components on a single datacollection module and components on other such modules. Other functionsthat a programmable logic component may provide may include generationof a voltage reference, such as a precise voltage reference for inputsignal condition detection. A programmable logic component may generate,set, reset, adjust, calibrate, or otherwise determine the voltage of thereference, its tolerance, and the like. Other functions of aprogrammable logic component may include enabling a digital phase lockloop to facilitate tracking slowly transitioning input signals, andfurther to facilitate detecting the phase of such signals. Relativephase detection may also be implemented, including phase relative totrigger signals, other analog inputs, on-board references (e.g.,on-board timers), and the like. A programmable logic component may beprogrammed to perform input signal peak voltage detection and controlinput signal circuitry, such as to implement auto-scaling of the inputto an operating voltage range of the input. Other functions that may beprogrammed into a programmable logic component may include determiningan appropriate sampling frequency for sampling inputs independently oftheir operating frequencies. A programmable logic component may beprogrammed to detect a maximum frequency among a plurality of inputsignals and set a sampling frequency for each of the input signals thatis greater than the detected maximum frequency.

A programmable logic component may be programmed to configure andcontrol data routing components, such as multiplexers, crosspointswitches, analog-to-digital converters, and the like, to implement adata collection template for the industrial environment. A datacollection template may be included in a program for a programmablelogic component. Alternatively, an algorithm that interprets a datacollection template to configure and control data routing resources inthe industrial environment may be included in the program.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to perform smart-band signalanalysis and testing. Results of such analysis and testing may includetriggering smart band data collection actions, that may includereconfiguring one or more data routing resources in the industrialenvironment. A programmable logic component may be configured to performa portion of smart band analysis, such as collection and validation ofsignal activity from one or more sensors that may be local to theprogrammable logic component. Smart band signal analysis results from aplurality of programmable logic components may be further processed byother programmable logic components, servers, machine learning systems,and the like to determine compliance with a smart band.

In embodiments, one or more programmable logic components in anindustrial environment may be programmed to control data routingresources and sensors for outcomes, such as reducing power consumption(e.g., powering on/off resources as needed), implementing security inthe industrial environment by managing user authentication, and thelike. In embodiments, certain data routing resources, such asmultiplexers and the like, may be configured to support certain inputsignal types. A programmable logic component may configure the resourcesbased on the type of signals to be routed to the resources. Inembodiments, the programmable logic component may facilitatecoordination of sensor and data routing resource signal type matching byindicating to a configurable sensor a protocol or signal type to presentto the routing resource. A programmable logic component may facilitatedetecting a protocol of a signal being input to a data routing resource,such as an analog crosspoint switch and the like. Based on the detectedprotocol, the programmable logic component may configure routingresources to facilitate support and efficient processing of theprotocol. In an example, a programmable logic component configured datacollection module in an industrial environment may implement anintelligent sensor interface specification, such as IEEE 1451.2intelligent sensor interface specification.

In embodiments, distributing programmable logic components across aplurality of data sensing, collection, and/or routing modules in anindustrial environment may facilitate greater functionality and localinter-operational control. In an example, modules may performoperational functions independently based on a program installed in oneor more programmable logic components associated with each module. Twomodules may be constructed with substantially identical physicalcomponents, but may perform different functions in the industrialenvironment based on the program(s) loaded into programmable logiccomponent(s) on the modules. In this way, even if one module were toexperience a fault, or be powered down, other modules may continue toperform their functions due at least in part to each having its ownprogrammable logic component(s). In embodiments, configuring a pluralityof programmable logic components distributed across a plurality of datacollection modules in an industrial environment may facilitatescalability in terms of conditions in the environment that may besensed, the number of data routing options for routing sensed datathroughout the industrial environment, the types of conditions that maybe sensed, the computing capability in the environment, and the like.

In embodiments, a programmable logic controller-configured datacollection and routing system may facilitate validation of externalsystems for use as storage nodes, such as for a distributed ledger, andthe like. A programmable logic component may be programmed to performvalidation of a protocol for communicating with such an external system,such as an external storage node.

In embodiments, programming of programmable logic components, such asCPLDs and the like may be performed to accommodate a range of datasensing, collection and configuration differences. In embodiments,reprogramming may be performed on one or more components when addingand/or removing sensors, when changing sensor types, when changingsensor configurations or settings, when changing data storageconfigurations, when embedding data collection template(s) into deviceprograms, when adding and/or removing data collection modules (e.g.,scaling a system), when a lower cost device is used that may limitfunctionality or resources over a higher cost device, and the like. Aprogrammable logic component may be programmed to propagate programs forother programmable components via a dedicated programmable logic deviceprogramming channel, via a daisy chain programming architecture, via amesh of programmable logic components, via a hub-and-spoke architectureof interconnected components, via a ring configuration (e.g., using acommunication token, and the like).

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed with drilling machines in anoil and gas harvesting environment, such as an oil and/or gas field. Adrilling machine has many active portions that may be operated,monitored, and adjusted during a drilling operation. Sensors to monitora crown block may be physically isolated from sensors for monitoring ablowout preventer and the like. To effectively maintain control of thiswide range and diverse disposition of sensors, programmable logiccomponents, such as Complex Programmable Logic Devices (“CPLD”) may bedistributed throughout the drilling machine. While each CPLD may beconfigured with a program to facilitate operation of a limited set ofsensors, at least portions of the CPLD may be connected by a dedicatedbus for facilitating coordination of sensor control, operation and use.In an example, a set of sensors may be disposed proximal to a mud pumpor the like to monitor flow, density, mud tank levels, and the like. Oneor more CPLD may be deployed with each sensor (or a group of sensors) tooperate the sensors and sensor signal routing and collection resources.The CPLD in this mud pump group may be interconnected by a dedicatedcontrol bus to facilitate coordination of sensor and data collectionresource control and the like. This dedicated bus may extend physicallyand/or logically beyond the mud pump control portion of the drillmachine so that CPLD of other portions (e.g., the crown block and thelike) may coordinate data collection and related activity throughportions of the drilling machine.

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed with compressors in an oiland gas harvesting environment, such as an oil and/or gas field.Compressors are used in the oil and gas industry for compressing avariety of gases and purposes include flash gas, gas lift, reinjection,boosting, vapor-recovery, casing head and the like. Collecting data fromsensors for these different compressor functions may requiresubstantively different control regimes. Distributing CPLDs programmedwith different control regimes is an approach that may accommodate thesediverse data collection requirements. One or more CPLDs may be disposedwith sets of sensors for the different compressor functions. A dedicatedcontrol bus may be used to facilitate coordination of control and/orprogramming of CPLDs in and across compressor instances. In an example,a CPLD may be configured to manage a data collection infrastructure forsensors disposed to collect compressor-related conditions for flash gascompression; a second CPLD or group of CPLDs may be configured to managea data collection infrastructure for sensors disposed to collectcompressor related conditions for vapor-recovery gas compression. Thesegroups of CPLDs may operate control programs.

In embodiments, a system for data collection in an industrialenvironment comprising distributed programmable logic devices connectedby a dedicated control bus may be deployed in a refinery with turbinesfor oil and gas production, such as with modular impulse steam turbines.A system for collection of data from impulse steam turbines may beconfigured with a plurality of condition sensing and collection modulesadapted for specific functions of an impulse steam turbine. DistributingCPLDs along with these modules can facilitate adaptable data collectionto suit individual installations. As an example, blade conditions, suchas tip rotational rate, temperature rise of the blades, impulsepressure, blade acceleration rate, and the like may be captured in datacollection modules configured with sensors for sensing these conditions.Other modules may be configured to collect data associated with valves(e.g., in a multi-valve configuration, one or more modules may beconfigured for each valve or for a set of valves), turbine exhaust(e.g., radial exhaust data collection may be configured differently thanaxial exhaust data collection), turbine speed sensing may be configureddifferently for fixed versus variable speed implementations, and thelike. Additionally, impulse gas turbine systems may be installed withother systems, such as combined cycle systems, cogeneration systems,solar power generation systems, wind power generation systems,hydropower generation systems, and the like. Data collectionrequirements for these installations may also vary. Using a CPLD-based,modular data collection system that uses a dedicated interconnection busfor the CPLDs may facilitate programming and/or reprogramming of eachmodule directly in place without having to shut down or physicallyaccess each module.

Referring to FIG. 14, an exemplary embodiment of a system for datacollection in an industrial environment comprising distributed CPLDsinterconnected by a bus for control and/or programming thereof isdepicted. An exemplary data collection module 7200 may comprise one ormore CPLDs 7206 for controlling one or more data collection systemresources, such as sensors 7202 and the like. Other data collectionresources that a CPLD may control may include crosspoint switches,multiplexers, data converters, and the like. CPLDs on a module may beinterconnected by a bus, such as a dedicated logic bus 7204 that mayextend beyond a data collection module to CPLDs on other data collectionmodules. Data collection modules, such as module 7200 may be configuredin the environment, such as on an industrial machine 7208 (e.g., animpulse gas turbine) and/or 7210 (e.g., a co-generation system), and thelike. Control and/or configuration of the CPLDs may be handled by acontroller 7212 in the environment. Data collection and routingresources and interconnection (not shown) may also be configured withinand among data collection modules 7200 as well as between and amongindustrial machines 7208 and 7210, and/or with external systems, such asInternet portals, data analysis servers, and the like to facilitate datacollection, routing, storage, analysis, and the like.

An example system for data collection in an industrial environmentincludes a number of industrial condition sensing and acquisitionmodules, with a programmable logic component disposed on each of themodules, where the programmable logic component controls a portion ofthe sensing and acquisition functional of the corresponding module. Thesystem includes communication bus that is dedicated to interconnectingthe at least one programmable logic component disposed on at least oneof the plurality of modules, wherein the communication bus extends toother programmable logic components on other sensing and acquisitionmodules.

In certain further embodiments, a system includes the programmable logiccomponent programmed via the communication bus, the communication busincluding a portion dedicated to programming of the programmable logiccomponents, controlling a portion of the sensing and acquisitionfunctionality of a module by a power control function such as:controlling power of a sensor, a multiplexer, a portion of the module,and/or controlling a sleep mode of the programmable logic component;controlling a portion of the sensing and acquisition functionality of amodule by providing a voltage reference to a sensor and/or ananalog-to-digital converter disposed on the module, by detecting arelative the phase of at least two analog signals derived from at leasttwo sensors disposed on the module; by controlling sampling of dataprovided by at least one sensor disposed on the module; by detecting apeak voltage of a signal provided by a sensor disposed on the module;and/or by configuring at least one multiplexer disposed on the module byspecifying to the multiplexer a mapping of at least one input and oneoutput. In certain embodiments, the communication bus extends to otherprogrammable logic components on other condition sensing and/oracquisition modules. In certain embodiments, a module may be anindustrial environment condition sensing module. In certain embodiments,a module control program includes an algorithm for implementing anintelligent sensor interface communication protocol, such as anIEEE1451.2 compatible intelligent sensor interface communicationprotocol. In certain embodiments, a programmable logic componentincludes configuring the programmable logic component and/or the sensingor acquisition module to implement a smart band data collectiontemplate. Example and non-limiting programmable logic components includefield programmable gate arrays, complex programmable logic devices,and/or microcontrollers.

An example system includes a drilling machine for oil and gas field use,with a condition sensing and/or acquisition module to monitor aspects ofa drilling machine. Without limitation, a further example systemincludes monitoring a compressor and/or monitoring an impulse steamengine.

In embodiments, a system for data collection in an industrialenvironment may include a trigger signal and at least one data signalthat share a common output of a signal multiplexer and upon detection ofa condition in the industrial environment, such as a state of thetrigger signal, the common output is switched to propagate either thedata signal or the trigger signal. Sharing an output between a datasignal and a trigger signal may also facilitate reducing a number ofindividually routed signals in an industrial environment. Benefits ofreducing individually routed signals may include reducing the number ofinterconnections between data collection module, thereby reducing thecomplexity of the industrial environment. Trade-offs for reducingindividually routed signals may include increasing sophistication oflogic at signal switching modules to implement the detection andconditional switching of signals. A net benefit of this added localizedlogic complexity may be an overall reduction in the implementationcomplexity of such a data collection system in an industrialenvironment.

Exemplary deployment environments may include environments with triggersignal channel limitations, such as existing data collection systemsthat do not have separate trigger support for transporting an additionaltrigger signal to a module with sufficient computing sophistication toperform trigger detection. Another exemplary deployment may includesystems that require at least some autonomous control for performingdata collection.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that switches between a firstinput, such as a trigger input and a second input, such as a data inputbased on a condition of the first input. A trigger input may bemonitored by a portion of the analog switch to detect a change in thesignal, such as from a lower voltage to a higher voltage relative to areference or trigger threshold voltage. In embodiments, a device thatmay receive the switched signal from the analog switch may monitor thetrigger signal for a condition that indicates a condition for switchingfrom the trigger input to the data input. When a condition of thetrigger input is detected, the analog switch may be reconfigured, todirect the data input to the same output that was propagating thetrigger output.

In embodiments, a system for data collection in an industrialenvironment may include an analog switch that directs a first input toan output of the analog switch until such time as the output of theanalog switch indicates that a second input should be directed to theoutput of the analog switch. The output of the analog switch maypropagate a trigger signal to the output. In response to the triggersignal propagating through the switch transitioning from a firstcondition (e.g., a first voltage below a trigger threshold voltagevalue) to a second condition (e.g., a second voltage above the triggerthreshold voltage value), the switch may stop propagating the triggersignal and instead propagate another input signal to the output. Inembodiments, the trigger signal and the other data signal may berelated, such as the trigger signal may indicate a presence of an objectbeing placed on a conveyer and the data signal represents a strainplaced on the conveyer.

In embodiments, to facilitate timely detection of the trigger condition,a rate of sampling of the output of the analog switch may be adjustable,so that, for example, the rate of sampling is higher while the triggersignal is propagated and lower when the data signal is propagated.Alternatively, a rate of sampling may be fixed for either the trigger orthe data signal. In embodiments, the rate of sampling may be based on apredefined time from trigger occurrence to trigger detection and may befaster than a minimum sample rate to capture the data signal.

In embodiments, routing a plurality of hierarchically organized triggersonto another analog channel may facilitate implementing a hierarchicaldata collection triggering structure in an industrial environment. Adata collection template to implement a hierarchical trigger signalarchitecture may include signal switch configuration and function datathat may facilitate a signal switch facility, such as an analogcrosspoint switch or multiplexer to output a first input trigger in ahierarchy, and based on the first trigger condition being detected,output a second input trigger in the hierarchy on the same output as thefirst input trigger by changing an internal mapping of inputs tooutputs. Upon detection of the second input trigger condition, theoutput may be switched to a data signal, such as data from a sensor inan industrial environment.

In embodiments, upon detection of a trigger condition, in addition toswitching from the trigger signal to a data signal, an alarm may begenerated and optionally propagated to a higher functioningdevice/module. In addition to switching to a data signal, upon detectionof a state of the trigger, sensors that otherwise may be disabled orpowered down may be energized/activated to begin to produce data for thenewly selected data signal. Activating might alternatively includesending a reset or refresh signal to the sensor(s).

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a gearbox of an industrial vehicle.Combining a trigger signal onto a signal path that is also used for adata signal may be useful in gearbox applications by reducing the numberof signal lines that need to be routed, while enabling advancedfunctions, such as data collection based on pressure changes in thehydraulic fluid and the like. As an example, a sensor may be configuredto detect a pressure difference in the hydraulic fluid that exceeds acertain threshold as may occur when the hydraulic fluid flow is directedback into the impeller to give higher torque at low speeds. The outputof such a sensor may be configured as a trigger for collecting dataabout the gearbox when operating at low speeds. In an example, a datacollection system for an industrial environment may have a multiplexeror switch that facilitates routing either a trigger or a data channelover a single signal path. Detecting the trigger signal from thepressure sensor may result in a different signal being routed throughthe same line that the trigger signal was routed by switching a set ofcontrols. A multiplexer may, for example, output the trigger signaluntil the trigger signal is detected as indicating that the outputshould be changed to the data signal. As a result of detecting thehigh-pressure condition, a data collection activity may be activated sothat data can be collected using the same line that was recently used bythe trigger signal.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a vehicle suspension for truck andcar operation. Vehicle suspension, particularly active suspension mayinclude sensors for detecting road events, suspension conditions, andvehicle data, such as speed, steering, and the like. These conditionsmay not always need to be detected, except, for example, upon detectionof a trigger condition. Therefore, combining the trigger conditionsignal and at least one data signal on a single physical signal routingpath could be implemented. Doing so may reduce costs due to fewerphysical connections required in such a data collection system. In anexample, a sensor may be configured to detect a condition, such as apothole, to which the suspension must react. Data from the suspensionmay be routed along the same signal routing path as this road conditiontrigger signal so that upon detection of the pothole, data may becollected that may facilitate determining aspects of the suspension'sreaction to the pothole.

In embodiments, a system for data collection in an industrialenvironment may include a system for routing a trigger signal onto adata signal path in association with a turbine for power generation in apower station. A turbine used for power generation may be retrofittedwith a data collection system that optimizes existing data signal linesto implement greater data collection functions. One such approachinvolves routing new sources of data over existing lines. Whilemultiplexing signals generally satisfies this need, combining a triggersignal with a data signal via a multiplexer or the like can furtherimprove data collection. In an example, a first sensor may include athermal threshold sensor that may measure the temperature of an aspectof a power generation turbine. Upon detection of that trigger (e.g., bythe temperature rising above the thermal threshold), a data collectionsystem controller may send a different data collection signal over thesame line that was used to detect the trigger condition. This may beaccomplished by a controller or the like sensing the trigger signalchange condition and then signaling to the multiplexer to switch fromthe trigger signal to a data signal to be output on the same line as thetrigger signal for data collection. In this example, when a turbine isdetected as having a portion that exceeds its safe thermal threshold, asecondary safety signal may be routed over the trigger signal path andmonitored for additional safety conditions, such as overheating and thelike.

Referring to FIG. 15, an embodiment of routing a trigger signal over adata signal path in a data collection system in an industrialenvironment is depicted. Signal multiplexer 7400 may receive a triggersignal on a first input from a sensor or other trigger source 7404 and adata signal on a second input from a sensor for detecting a temperatureassociated with an industrial machine in the environment. Themultiplexer 7400 may be configured to output the trigger signal onto anoutput signal path 7406. A data collection module 7410 may process thesignal on the data path 7406 looking for a change in the signalindicative of a trigger condition provided from the trigger sensor 7404through the multiplexer 7400. Upon detection, a control output signal7408 may be changed and thereby control the multiplexer 7400 to startoutputting data from the temperature probe 7402 by switching an internalswitch or the like that may control one or more of the inputs that maybe routed to the output 7406. Data collection module 7410 may activate adata collection template in response to the detected trigger that mayinclude switching the multiplexer and collecting data into triggereddata storage 7412. Upon completion of the data collection activity,multiplexer control output signal 7408 may revert to its initialcondition so that trigger sensor 7404 may be monitored again.

An example system for data collection in an industrial environmentincludes an analog switch that directs a first input to an output of theanalog switch until such time as the output of the analog switchindicates that a second input should be directed to the output of theanalog switch. In certain further embodiments, the example systemincludes: where the output of the analog switch indicated that thesecond input should be directed to the output based on the outputtransitioning from a pending condition to a triggered condition; whereinthe triggered condition includes detecting the output presenting avoltage above a trigger voltage value; routing a number of signals withthe analog switch from inputs on the analog switch to outputs on theanalog switch in response to the output of the analog switch indicatingthat the second input should be directed to the output; sampling theoutput of the analog switch at a rate that exceeds a rate of transitionfor a number of signals input to the analog switch; and/or generating analarm signal when the output of the analog switch indicates that asecond input should be directed to the output of the analog switch.

An example system for data collection in an industrial environmentincludes an analog switch that switches between a first input and asecond input based on a condition of the first input. In certain furtherembodiments, the condition of the first input comprises the first inputpresenting a triggered condition, and/or the triggered conditionincludes detecting the first input presenting a voltage above a triggervoltage value. In certain embodiments, the analog switch includesrouting a plurality of signals with the analog from inputs on the analogswitch to outputs on the analog switch based on the condition of thefirst input, sampling an input of the analog switch at a rate thatexceeds a rate of transition for a plurality of signals input to theanalog switch, and/or generating an alarm signal based on the conditionof the first input.

An example system for data collection in an industrial environmentincludes a trigger signal and at least one data signal that share acommon output of a signal multiplexer, and upon detection of apredefined state of the trigger signal, the common output is configuredto propagate the at least one data signal through the signalmultiplexer. In certain further embodiments, the signal multiplexer isan analog multiplexer, the predefined state of the trigger signal isdetected on the common output, detection of the predefined state of thetrigger signal includes detecting the common output presenting a voltageabove a trigger voltage value, the multiplexer includes routing aplurality of signals with the multiplexer from inputs on the multiplexerto outputs on the multiplexer in response to detection of the predefinedstate of the trigger signal, the multiplexer includes sampling theoutput of the multiplexer at a rate that exceeds a rate of transitionfor a plurality of signals input to the multiplexer, the multiplexerincludes generating an alarm in response to detection of the predefinedstate of the trigger signal, and/or the multiplexer includes activatingat least one sensor to produce the at least one data signal. Withoutlimitation, example systems include: monitoring a gearbox of anindustrial vehicle by directing a trigger signal representing acondition of the gearbox to an output of the analog switch until suchtime as the output of the analog switch indicates that a second inputrepresenting a condition of the gearbox related to the trigger signalshould be directed to the output of the analog switch; monitoring asuspension system of an industrial vehicle by directing a trigger signalrepresenting a condition of the suspension to an output of the analogswitch until such time as the output of the analog switch indicates thata second input representing a condition of the suspension related to thetrigger signal should be directed to the output of the analog switch;and/or monitoring a power generation turbine by directing a triggersignal representing a condition of the power generation turbine to anoutput of the analog switch until such time as the output of the analogswitch indicates that a second input representing a condition of thepower generation turbine related to the trigger signal should bedirected to the output of the analog switch.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for a set of collection band parameters and upon detection ofa parameter from the set of collection band parameters in the signal,configures collection of data from a set of sensors based on thedetected parameter. The set of selected sensors, the signal, and the setof collection band parameters may be part of a smart bands datacollection template that may be used by the system when collecting datain an industrial environment. A motivation for preparing a smart-bandsdata collection template may include monitoring a set of conditions ofan industrial machine to facilitate improved operation, reduce downtime, preventive maintenance, failure prevention, and the like. Based onanalysis of data about the industrial machine, such as those conditionsthat may be detected by the set of sensors, an action may be taken, suchas notifying a user of a change in the condition, adjusting operatingparameters, scheduling preventive maintenance, triggering datacollection from additional sets of sensors, and the like. An example ofdata that may indicate a need for some action may include changes thatmay be detectable through trends present in the data from the set ofsensors. Another example is trends of analysis values derived from theset of sensors.

In embodiments, the set of collection band parameters may include valuesreceived from a sensor that is configured to sense a condition of theindustrial machine (e.g., bearing vibration). However, a set ofcollection band parameters may instead be a trend of data received fromthe sensor (e.g., a trend of bearing vibration across a plurality ofvibration measurements by a bearing vibration sensor). In embodiments, aset of collection band parameters may be a composite of data and/ortrends of data from a plurality of sensors (e.g., a trend of data fromon-axis and off-axis vibration sensors). In embodiments, when a datavalue derived from one or more sensors as described herein issufficiently close to a value of data in the set of collection bandparameters, the data collection activity from the set of sensors may betriggered. Alternatively, a data collection activity from the set ofsensors may be triggered when a data value derived from the one or moresensors (e.g., trends and the like) falls outside of a set of collectionband parameters. In an example, a set of data collection band parametersfor a motor may be a range of rotational speeds from 95% to 105% of aselect operational rotational speed. So long as a trend of rotationalspeed of the motor stays within this range, a data collection activitymay be deferred. However, when the trend reaches or exceeds this range,then a data collection activity, such as one defined by a smart bandsdata collection template may be triggered.

In embodiments, triggering a data collection activity, such as onedefined by a smart bands data collection template, may result in achange to a data collection system for an industrial environment thatmay impact aspects of the system such as data sensing, switching,routing, storage allocation, storage configuration, and the like. Thischange to the data collection system may occur in near real time to thedetection of the condition; however, it may be scheduled to occur in thefuture. It may also be coordinated with other data collection activitiesso that active data collection activities, such as a data collectionactivity for a different smart bands data collection template, cancomplete prior to the system being reconfigured to meet the smart bandsdata collection template that is triggered by the sensed conditionmeeting the smart bands data collection trigger.

In embodiments, processing of data from sensors may be cumulative overtime, over a set of sensors, across machines in an industrialenvironment, and the like. While a sensed value of a condition may besufficient to trigger a smart bands data collection template activity,data may need to be collected and processed over time from a pluralityof sensors to generate a data value that may be compared to a set ofdata collection band parameters for conditionally triggering the datacollection activity. Using data from multiple sensors and/or processingdata, such as to generate a trend of data values and the like mayfacilitate preventing inconsequential instances of a sensed data valuebeing outside of an acceptable range from causing unwarranted smartbands data collection activity. In an example, if a vibration from abearing is detected outside of an acceptable range infrequently, thentrending for this value over time may be useful to detect if thefrequency is increasing, decreasing, or staying substantially constantor within a range of values. If the frequency of such a value is foundto be increasing, then such a trend is indicative of changes occurringin operation of the industrial machine as experienced by the bearing. Anacceptable range of values of this trended vibration value may beestablished as a set of data collection band parameters against whichvibration data for the bearing will be monitored. When the trendedvibration value is outside of this range of acceptable values, a smartbands data collection activity may be activated.

In embodiments, a system for data collection in an industrialenvironment that supports smart band data collection templates may beconfigured with data processing capability at a point of sensing of oneor more conditions that may trigger a smart bands data collectiontemplate data collection activity, such as: by use of an intelligentsensor that may include data processing capabilities; by use of aprogrammable logic component that interfaces with a sensor and processesdata from the sensor; by use of a computer processor, such as amicroprocessor and the like disposed proximal to the sensor; and thelike. In embodiments, processing of data collected from one or moresensors for detecting a smart bands template data collection activitymay be performed by remote processors, servers, and the like that mayhave access to data from a plurality of sensors, sensor modules,industrial machines, industrial environments, and the like.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors anindustrial environment for a set of parameters, and upon detection of atleast one parameter, configures the collection of data from a set ofsensors and causes a data storage controller to adapt a configuration ofdata storage facilities to support collection of data from the set ofsensors based on the detected parameter. The methods and systemsdescribed herein for conditionally changing a configuration of a datacollection system in an industrial environment to implement a smartbands data collection template may further include changes to datastorage architectures. As an example, a data storage facility may bedisposed on a data collection module that may include one or moresensors for monitoring conditions in an industrial environment. Thislocal data storage facility may typically be configured for rapidmovement of sensed data from the module to a next level sensing orprocessing module or server. When a smart bands data collectioncondition is detected, sensor data from a plurality of sensors may needto be captured concurrently. To accommodate this concurrent collection,the local memory may be reconfigured to capture data from each of theplurality of sensors in a coordinated manner, such as repeatedlysampling each of the sensors synchronously, or with a known offset, andthe like, to build up a set of sensed data that may be much larger thanwould typically be captured and moved through the local memory. Astorage control facility for controlling the local storage may monitorthe movement of sensor data into and out of the local data storage,thereby ensuring safe movement of data from the plurality of sensors tothe local data storage and on to a destination, such as a server,networked storage facility, and the like. The local data storagefacility may be configured so that data from the set of sensorsassociated with a smart bands data collection template are securelystored and readily accessible as a set of smart band data to facilitateprocessing the smart band-specific data. As an example, local storagemay comprise non-volatile memory (NVM). To prepare for data collectionin response to a smart band data collection template being triggered,portions of the NVM may be erased to prepare the NVM to receive data asindicated in the template.

In embodiments, multiple sensors may be arranged into a set of sensorsfor condition-specific monitoring. Each set, which may be a logical setof sensors, may be selected to provide information about elements in anindustrial environment that may provide insight into potential problems,root causes of problems, and the like. Each set may be associated with acondition that may be monitored for compliance with an acceptable rangeof values. The set of sensors may be based on a machine architecture,hierarchy of components, or a hierarchy of data that contributes to afinding about a machine that may usefully be applied to maintaining orimproving performance in the industrial environment. Smart band sensorsets may be configured based on expert system analysis of complexconditions, such as machine failures and the like. Smart band sensorsets may be arranged to facilitate knowledge gathering independent of aparticular failure mode or history. Smart band sensor sets may bearranged to test a suggested smart band data collection template priorto implementing it as part of an industrial machine operations program.Gathering and processing data from sets of sensors may facilitatedetermining which sensors contribute meaningful data to the set, andthose sensors that do not contribute can be removed from the set. Smartband sensor sets may be adjusted based on external data, such asindustry studies that indicate the types of sensor data that is mosthelpful to reduce failures in an industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a data collection system that monitors at leastone signal for compliance to a set of collection band conditions andupon detection of a lack of compliance, configures the collection ofdata from a predetermined set of sensors associated with the monitoredsignal. Upon detection of a lack of compliance, a collection bandtemplate associated with the monitored signal may be accessed, andresources identified in the template may be configured to perform thedata collection. In embodiments, the template may identify sensors toactivate, data from the sensors to collect, duration of collection orquantity of data to be collected, destination (e.g., memory structure)to store the collected data, and the like. In embodiments, a smart bandmethod for data collection in an industrial environment may includeperiodic collection of data from one or more sensors configured to sensea condition of an industrial machine in the environment. The collecteddata may be checked against a set of criteria that define an acceptablerange of the condition. Upon validation that the collected data iseither approaching one end of the acceptable limit or is beyond theacceptable range of the condition, data collection may commence from asmart-band group of sensors associated with the sensed condition basedon a smart-band collection protocol configured as a data collectiontemplate. In embodiments, an acceptable range of the condition is basedon a history of applied analytics of the condition. In embodiments, uponvalidation of the acceptable range being exceeded, data storageresources of a module in which the sensed condition is detected may beconfigured to facilitate capturing data from the smart band group ofsensors.

In embodiments, monitoring a condition to trigger a smart band datacollection template data collection action may be: in response to: aregulation, such as a safety regulation; in response to an upcomingactivity, such as a portion of the industrial environment being shutdown for preventive maintenance; in response to sensor data missing fromroutine data collection activities; and the like. In embodiments, inresponse to a faulty sensor or sensor data missing from a smart bandtemplate data collection activity, one or more alternate sensors may betemporarily included in the set of sensors so as to provide data thatmay effectively substitute for the missing data in data processingalgorithms.

In embodiments, smart band data collection templates may be configuredfor detecting and gathering data for smart band analysis coveringvibration spectra, such as vibration envelope and current signature forspectral regions or peaks that may be combinations of absolute frequencyor factors of machine related parameters, vibration time waveforms fortime-domain derived calculations including, without limitation: RMSoverall, peak overall, true peak, crest factor, and the like; vibrationvectors, spectral energy humps in various regions (e.g., low-frequencyregion, high frequency region, low orders, and the like);pressure-volume analysis and the like.

In embodiments, a system for data collection that applies smart banddata collection templates may be applied to an industrial environment,such as ball screw actuators in an automated production environment.Smart band analysis may be applied to ball screw actuators in industrialenvironments such as precision manufacturing or positioning applications(e.g., semiconductor photolithography machines, and the like). As atypical primary objective of using a ball screw is for precisepositioning, detection of variation in the positioning mechanism canhelp avoid costly defective production runs. Smart bands triggering anddata collection may help in such applications by detecting, throughsmart band analysis, potential variations in the positioning mechanismsuch as in the ball screw mechanism, a worm drive, a linear motor, andthe like. In an example, data related to a ball screw positioning systemmay be collected with a system for data collection in an industrialenvironment as described herein. A plurality of sensors may beconfigured to collect data such as screw torque, screw direction, screwspeed, screw step, screw home detection, and the like. Some portion ofthis data may be processed by a smart bands data analysis facility todetermine if variances, such as trends in screw speed as a function oftorque, approach or exceed an acceptable threshold. Upon such adetermination, a data collection template for the ball screw productionsystem may be activated to configure the data sensing, routing, andcollection resources of the data collection system to perform datacollection to facilitate further analysis. The smart band datacollection template facilitates rapid collection of data from othersensors than screw speed and torque, such as position, direction,acceleration, and the like by routing data from corresponding sensorsover one or more signal paths to a data collector. The duration andorder of collection of the data from these sources may be specified inthe smart bands data collection template so that data required forfurther analysis is effectively captured.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to ventilation systems in miningenvironments. Ventilation provides a crucial role in mining safety.Early detection of potential problems with ventilation equipment can beaided by applying a smart bands approach to data collection in such anenvironment. Sensors may be disposed for collecting information aboutventilation operation, quality, and performance throughout a miningoperation. At each ventilation device, ventilation-related elements,such as fans, motors, belts, filters, temperature gauges, voltage,current, air quality, poison detection, and the like may be configuredwith a corresponding sensor. While variation in any one element (e.g.,air volume per minute, and the like) may not be indicative of a problem,smart band analysis may be applied to detect trends over time that maybe suggestive of potential problems with ventilation equipment. Toperform smart bands analysis, data from a plurality of sensors may berequired to form a basis for analysis. By implementing data collectionsystems for ventilation stations, data from a ventilation system may becaptured. In an example, a smart band analysis may be indicated for aventilation station. In response to this indication, a data collectionsystem may be configured to collect data by routing data from sensorsdisposed at the ventilation station to a central monitoring facilitythat may gather and analyze data from several ventilation stations.

In embodiments, a system for data collection that applies smart banddata collection templates to configure and utilize data collection androuting infrastructure may be applied to drivetrain data collection andanalysis in mining environments. A drivetrain, such as a drivetrain fora mining vehicle, may include a range of elements that could benefitfrom use of the methods and systems of data collection in an industrialenvironment as described herein. In particular, smart band-based datacollection may be used to collect data from heavy duty mining vehicledrivetrains under certain conditions that may be detectable by smartbands analysis. A smart bands-based data collection template may be usedby a drivetrain data collection and routing system to configure sensors,data paths, and data collection resources to perform data collectionunder certain circumstances, such as those that may indicate anunacceptable trend of drivetrain performance. A data collection systemfor an industrial drivetrain may include sensing aspects of anon-steering axle, a planetary steering axle, driveshafts, (e.g., mainand wing shafts), transmissions, (e.g., standard, torque converters,long drop), and the like. A range of data related to these operationalparts may be collected. However, data for support and structural membersthat support the drivetrain may also need to be collected for thoroughsmart band analysis. Therefore, collection across this wide range ofdrivetrain-related components may be triggered based on a smart bandanalysis determination of a need for this data. In an example, a smartband analysis may indicate potential slippage between a main and wingdriveshaft that may represented by an increasing trend in response delaytime of the wing drive shaft to main drive shaft operation. In responseto this increasing trend, data collection modules disposed throughoutthe mining vehicle's drive train may be configured to route data fromlocal sensors to be collected and analyzed by data collectors. Miningvehicle drivetrain smart based data collection may include a range oftemplates based on which type of trend is detected. If a trend relatedto a steering axle is detected, a data collection template to beimplemented may be different in sensor content, duration, and the likethan for a trend related to power demand for a normalized payload. Eachtemplate could configure data sensing, routing, and collection resourcesthroughout the vehicle drive train accordingly.

Referring to FIG. 16, a system for data collection in an industrialenvironment that facilitates data collection for smart band analysis isdepicted. A system for data collection in an industrial environment mayinclude a smart band analysis data collection template repository 7600in which smart band templates 7610 for data collection systemconfiguration and collection of data may be stored and accessed by adata collection controller 7602. The templates 7610 may include datacollection system configuration 7604 and operation information 7606 thatmay identify sensors, collectors, signal paths, and information forinitiation and coordination of collection, and the like. A controller7602 may receive an indication, such as a command from a smart bandanalysis facility 7608 to select and implement a specific smart bandtemplate 7610. The controller 7602 may access the template 7610 andconfigure the data collection system resources based on the informationin that template. In embodiments, the template may identify: specificsensors; a multiplexer/switch configuration, data collectiontrigger/initiation signals and/or conditions, time duration and/oramount of data for collection; destination of collected data;intermediate processing, if any; and any other useful information,(e.g., instance identifier, and the like). The controller 7602 mayconfigure and operate the data collection system to perform thecollection for the smart band template and optionally return the systemconfiguration to a previous configuration.

An example system for data collection in an industrial environmentincludes a data collection system that monitors at least one signal fora set of collection band parameters and, upon detection of a parameterfrom the set of collection band parameters, configures portions of thesystem and performs collection of data from a set of sensors based onthe detected parameter. In certain further embodiments, the signalincludes an output of a sensor that senses a condition in the industrialenvironment, where the set of collection band parameters comprisesvalues derivable from the signal that are beyond an acceptable range ofvalues derivable from the signal; where the at least one signal includesan output of a sensor that senses a condition in the industrialenvironment; wherein configuring portions of the system includesconfiguring a storage facility to accept data collected from the set ofsensors; where configuring portions of the system includes configuring adata routing portion includes at least one of: an analog crosspointswitch, a hierarchical multiplexer, an analog-to-digital converter, anintelligent sensor, and/or a programmable logic component; whereindetection of a parameter from the set of collection band parameterscomprises detecting a trend value for the signal being beyond anacceptable range of trend values; and/or where configuring portions ofthe system includes implementing a smart band data collection templateassociated with the detected parameter. In certain embodiments, a datacollection system monitors a signal for data values within a set ofacceptable data values that represent acceptable collection bandconditions for the signal and, upon detection of a data value for the atleast one signal outside of the set of acceptable data values, triggersa data collection activity that causes collecting data from apredetermined set of sensors associated with the monitored signal. Incertain further embodiment, a data collection system includes the signalincluding an output of a sensor that senses a condition in theindustrial environment; where the set of acceptable data value includesvalues derivable from the signal that are within an acceptable range ofvalues derivable from the signal; configuring a storage facility of thesystem to facilitate collecting data from the predetermined set ofsensors in response to the detection of a data value outside of the setof acceptable data values; configuring a data routing portion of thesystem including an analog crosspoint switch, a hierarchicalmultiplexer, an analog-to-digital converter, an intelligent sensor,and/or a programmable logic component in response to detecting a datavalue outside of the set of acceptable data values; where detection of adata value for the signal outside of the set of acceptable data valuesincludes detecting a trend value for the signal being beyond anacceptable range of trend values; and/or where the data collectionactivity is defined by a smart band data collection template associatedwith the detected parameter.

An example method for data collection in an industrial environmentcomprising includes an operation to collect data from sensor(s)configured to sense a condition of an industrial machine in theenvironment; an operation to check the collected data against a set ofcriteria that define an acceptable range of the condition; and inresponse to the collected data violating the acceptable range of thecondition, an operation to collect data from a smart-band group ofsensors associated with the sensed condition based on a smart-bandcollection protocol configured as a smart band data collection template.In certain further embodiments, a method includes where violating theacceptable range of the condition includes a trend of the data from thesensor(s) approaching a maximum value of the acceptable range; where thesmart-band group of sensors is defined by the smart band data collectiontemplate; where the smart band data collection template includes a listof sensors to activate, data from the sensors to collect, duration ofcollection of data from the sensors, and/or a destination location forstoring the collected data; where collecting data from a smart-bandgroup of sensors includes configuring at least one data routing resourceof the industrial environment that facilitates routing data from thesmart band group of sensors to a plurality of data collectors; and/orwhere the set of criteria includes a range of trend values derived byprocessing the data from sensor(s).

Without limitation, an example system monitors a ball screw actuator inan automated production environment, and monitors at least one signalfrom the ball screw actuator for a set of collection band parametersand, upon detection of a parameter from the set of collection bandparameters, configures portions of the system and performs collection ofdata from a set of sensors disposed to monitor conditions of the ballscrew actuator based on the detected parameter; another example systemmonitors a ventilation system in a mining environment, and monitors atleast one signal from the ventilation system for a set of collectionband parameters and, upon detection of a parameter from the set ofcollection band parameters, configures portions of the system andperforms collection of data from a set of sensors disposed to monitorconditions of the ventilation system based on the detected parameter; anexample system monitors a drivetrain of a mining vehicle, and monitorsat least one signal from the drive train for a set of collection bandparameters and, upon detection of a parameter from the set of collectionband parameters, configures portions of the system and performscollection of data from a set of sensors disposed to monitor conditionsof the drivetrain based on the detected parameter.

In embodiments, a system for data collection in an industrialenvironment may automatically configure local and remote data collectionresources and may perform data collection from a plurality of systemsensors that are identified as part of a group of sensors that producedata that is required to perform operational deflection shape rendering.In embodiments, the system sensors are distributed throughout structuralportions of an industrial machine in the industrial environment. Inembodiments, the system sensors sense a range of system conditionsincluding vibration, rotation, balance, friction, and the like. Inembodiments, automatically configuring is in response to a condition inthe environment being detected outside of an acceptable range ofcondition values. In embodiments, a sensor in the identified group ofsystem sensors senses the condition.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection plan, such as a template, tocollect data from a plurality of system sensors distributed throughout amachine to facilitate automatically producing an operational deflectionshape visualization (“ODSV”) based on machine structural information anda data set used to produce an ODSV of the machine.

In embodiments, a system for data collection in an industrialenvironment may configure a data collection template for collecting datain an industrial environment by identifying sensors disposed for sensingconditions of preselected structural members of an industrial machine inthe environment based on an ODSV of the industrial machine. Inembodiments, the template may include an order and timing of datacollection from the identified sensors.

In embodiments, methods and systems for data collection in an industrialenvironment may include a method of establishing an acceptable range ofsensor values for a plurality of industrial machine condition sensors byvalidating an operational deflection shape visualization of structuralelements of the machine as exhibiting deflection within an acceptablerange, wherein data from the plurality of sensors used in the validatedODSV define the acceptable range of sensor values.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of data sources, such as sensors,that may be grouped for coordinated data collection to provide datarequired to produce an ODSV. Information regarding the sensors to group,data collection coordination requirements, and the like may be retrievedfrom an ODSV data collection template. Coordinated data collection mayinclude concurrent data collection. To facilitate concurrent datacollection from a portion of the group of sensors, sensor routingresources of the system for data collection may be configured, such asby configuring a data multiplexer to route data from the portion of thegroup of sensors to which it connects to data collectors. Inembodiments, each such source that connects an input of the multiplexermay be routed within the multiplexer to separate outputs so that datafrom all of the connected sources may be routed on to data collectionelements of the industrial environment. In embodiments, the multiplexermay include data storage capabilities that may facilitate sharing acommon output for at least a portion of the inputs. In embodiments, amultiplexer may include data storage capabilities and data bus-enabledoutputs so that data for each source may be captured in a memory andtransmitted over a data bus, such as a data bus that is common to theoutputs of the multiplexer. In embodiments, sensors may be smart sensorsthat may include data storage capabilities and may send data from thedata storage to the multiplexer in a coordinated manner that supportsuse of a common output of the multiplexer and/or use of a common databus.

In embodiments, a system for data collection in an industrialenvironment may comprise templates for configuring the data collectionsystem to collect data from a plurality of sensors to perform ODSV for aplurality of deflection shapes. Individual templates may be configuredfor visualization of looseness, soft joints, bending, twisting, and thelike. Individual deflection shape data collection templates may beconfigured for different portions of a machine in an industrialenvironment.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthat may include visualization of locations of sensors that contributeddata to the visualization. In the visualization, each sensor thatcontributed data to generate the visualization may be indicated by avisual element. The visual element may facilitate user access toinformation about the sensor, such as location, type, representativedata contributed, path of data from the sensor to a data collector, adeflection shape template identifier, a configuration of a switch ormultiplexer through which the data is routed, and the like. The visualelement may be determined by associating sensor identificationinformation received from a sensor with information, such as a sensormap, that correlates sensor identification information with physicallocation in the environment. The information may appear in thevisualization in response to the visual element representing the sensorbeing selected, such as by a user positioning a cursor on the sensorvisual element.

In embodiments, ODSV may benefit from data satisfying a phaserelationship requirement. A data collection system in the environmentmay be configured to facilitate collecting data that complies with thephase relationship requirement. Alternatively, the data collectionsystem may be configured to collect data from a plurality of sensorsthat contains data that satisfies the phase relationship requirementsbut may also include data that does not. A post processing operationthat may access phase detection data may select a subset of thecollected data.

In embodiments, a system for data collection in an industrialenvironment may include a multiplexer receiving data from a plurality ofsensors and multiplexing the received data for delivery to a datacollector. The data collector may process the data to facilitate ODSV.ODSV may require data from several different sensors, and may benefitfrom using a reference signal, such as data from a sensor, whenprocessing data from the different sensors. The multiplexer may beconfigured to provide data from the different sensors, such as byswitching among its inputs over time so that data from each sensor maybe received by the data collector. However, the multiplexer may includea plurality of outputs so that at least a portion of the inputs may berouted to least two of the plurality of outputs. Therefore, inembodiments, a multiple output multiplexer may be configured tofacilitate data collection that may be suitable for ODSV by routing areference signal from one of its inputs (e.g., data from anaccelerometer) to one of its outputs and multiplexing data from aplurality of its outputs onto one or more of its outputs whilemaintaining the reference signal output routing. A data collector maycollect the data from the reference output and use that to align themultiplexed data from the other sensors.

In embodiments, a system for data collection in an industrialenvironment may facilitate ODSV through coordinated data collectionrelated to conveyors for mining applications. Mining operations may relyon conveyor systems to move material, supplies, and equipment into andout of a mine. Mining operations may typically operate around the clock;therefore, conveyor downtime may have a substantive impact onproductivity and costs. Advanced analysis of conveyor and relatedsystems that focuses on secondary affects that may be challenging todetect merely through point observation may be more readily detected viaODSV. Capturing operational data related to vibration, stresses, and thelike can facilitate ODSV. However, coordination of data capture providesmore reliable results. Therefore, a data collection system that may havesensors dispersed throughout a conveyor system can be configured tofacilitate such coordinated data collection. In an example, capture ofdata affecting structural components of a conveyor, such as; landingpoints and the horizontal members that connect them and support theconveyer between landing points; conveyer segment handoff points; motormounts; mounts of conveyer rollers and the like may need to becoordinated with data related to conveyor dynamic loading, drivesystems, motors, gates, and the like. A system for data collection in anindustrial environment, such as a mining environment may include datasensing and collection modules placed throughout the conveyor atlocations such as segment handoff points, drive systems, and the like.Each module may be configured by one or more controllers, such asprogrammable logic controllers, that may be connected through a physicalor logical (e.g., wireless) communication bus that aids in performingcoordinated data collection. To facilitate coordination, a referencesignal, such as a trigger and the like, may be communicated among themodules for use when collecting data. In embodiments, data collectionand storage may be performed at each module so as to reduce the need forreal-time transfer of sensed data throughout the mining environment.Transfer of data from the modules to an ODSV processing facility may beperformed after collection, or as communication bandwidth between themodules and the processing facility allows. ODSV can provide insightinto conditions in the conveyer, such as deflection of structuralmembers that may, over time cause premature failure. Coordinated datacollection with a data collection system for use in an industrialenvironment, such as mining, can enable ODSV that may reduce operatingcosts by reducing downtime due to unexpected component failure.

In embodiments, a system for data collection in an industrialenvironment may facilitate operational deflection shape visualizationthrough coordinated data collection related to fans for miningapplications. Fans provide a crucial function in mining operations ofmoving air throughout a mine to provide ventilation, equipment cooling,combustion exhaust evacuation, and the like. Ensuring reliable and oftencontinuous operation of fans may be critical for miner safety andcost-effective operations. Dozens or hundreds of fans may be used inlarge mining operations. Fans, such as fans for ventilation managementmay include circuit, booster, and auxiliary types. High capacityauxiliary fans may operate at high speeds, over 2500 RPMs. PerformingODSV may reveal important reliability information about fans deployed ina mining environment. Collecting the range of data needed for ODSV ofmining fans may be performed by a system for collecting data inindustrial environments as described herein. In embodiments, sensingelements, such as intelligent sensing and data collection modules may bedeployed with fans and/or fan subsystems. These modules may exchangecollection control information (e.g., over a dedicated control bus andthe like) so that data collection may be coordinated in time and phaseto facilitate ODSV.

A large auxiliary fan for use in mining may be constructed fortransportability into and through the mine and therefore may include afan body, intake and outlet ports, dilution valves, protection cage,electrical enclosure, wheels, access panels, and other structural and/oroperational elements. The ODSV of such an auxiliary fan may requirecollection of data from many different elements. A system for datacollection may be configured to sense and collect data that may becombined with structural engineering data to facilitate ODSV for thistype of industrial fan.

Referring to FIG. 17, an embodiment of a system for data collection inan industrial environment that performs coordinated data collectionsuitable for ODSV is depicted. A system for data collection in anindustrial environment may include a ODSV data collection templaterepository 7800 in which ODSV templates 7810 for data collection systemconfiguration and collection of data may be stored and accessed by asystem for data collection controller 7802. The templates 7810 mayinclude data collection system configuration 7804 and operationinformation 7806 that may identify sensors, collectors, signal paths,reference signal information, information for initiation andcoordination of collection, and the like. A controller 7802 may receivean indication, such as a command from a ODSV analysis facility 7808 toselect and implement a specific ODSV template 7810. The controller 7802may access the template 7810 and configure the data collection systemresources based on the information in that template. In embodiments, thetemplate may identify specific sensors, multiplexer/switchconfiguration, reference signals for coordinating data collection, datacollection trigger/initiation signals and/or conditions, time duration,and/or amount of data for collection, destination of collected data,intermediate processing, if any, and any other useful information (e.g.,instance identifier, and the like). The controller 7802 may configureand operate the data collection system to perform the collection for theODSV template and optionally return the system configuration to aprevious configuration.

An example method of data collection for performing ODSV in anindustrial environment includes automatically configuring local andremote data collection resources and collecting data from a number ofsensors using the configured resources, where the number of sensorsinclude a group of sensors that produce data that is required to performthe ODSV. In certain further embodiments, an example method furtherincludes where the sensors are distributed throughout structuralportions of an industrial machine in the industrial environment; wherethe sensors sense a range of system conditions including vibration,rotation, balance, and/or friction; where the automatically configuringis in response to a condition in the environment being detected outsideof an acceptable range of condition values; where the condition issensed by a sensor in a group of system sensors; where automaticallyconfiguring includes configuring a signal switching resource toconcurrently connect a portion of the group of sensors to datacollection resources; and/or where the signal switching resource isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV.

An example method of data collection in an industrial environmentincludes configuring a data collection plan to collect data from anumber of system sensors distributed throughout a machine in theindustrial environment, the plan based on machine structural informationand an indication of data needed to produce an ODSV of the machine;configuring data sensing, routing and collection resources in theenvironment based on the data collection plan; and collecting data basedon the data collection plan. In certain further embodiments, an examplemethod further includes: producing the ODSV; where the configuring datasensing, routing, and collection resources is in response to a conditionin the environment being detected outside of an acceptable range ofcondition values; where the condition is sensed by a sensor identifiedin the data collection plan; where configuring resources includesconfiguring a signal switching resource to concurrently connect theplurality of system sensors to data collection resources; and/or wherethe signal switching resource is configured to maintain a connectionbetween a reference sensor and the data collection resources throughouta period of collecting data from the sensors to perform ODSV.

An example system for data collection in an industrial environmentincludes: a number of sensors disposed throughout the environment;multiplexer that connects signals from the plurality of sensors to datacollection resources; and a processor for processing data collected fromthe number of sensors in response to the data collection template, wherethe processing results in an ODSV of a portion of a machine disposed inthe environment. In certain further embodiments, an example systemincludes: where the ODSV collection template further identifies acondition in the environment on which performing data collection fromthe identified sensors is dependent; where the condition is sensed by asensor identified in the ODSV data collection template; where the datacollection template specified inputs of the multiplexer to concurrentlyconnect to data collection resources; where the multiplexer isconfigured to maintain a connection between a reference sensor and thedata collection resources throughout a period of collecting data fromthe sensors to perform ODSV; where the ODSV data collection templatespecifies data collection requirements for performing ODSV forlooseness, soft joints, bending, and/or twisting of a portion of amachine in the industrial environment; and/or where the ODSV collectiontemplate specifies an order and timing of data collection from aplurality of identified sensors.

An example method of monitoring a mining conveyer for performing ODSV ofthe conveyer includes automatically configuring local and remote datacollection resources and collecting data from a number of sensorsdisposed to sense the mining conveyor using the configured resources,wherein the plurality of sensors comprise a group of sensors thatproduce data that is required to perform the operational deflectionshape visualization of a portion of the conveyor. An example method ofmonitoring a mining fan for performing ODSV of the fan includesautomatically configuring local and remote data collection resourcescollecting data from a number of sensors disposed to sense the fan usingthe configured resources, and where the number of sensors include agroup of sensors that produce data that is sufficient or required toperform ODSV of a portion of the fan.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that facilitatessuccessive multiplexing of input data channels according to aconfigurable hierarchy, such as a user configurable hierarchy. Thesystem for data collection in an industrial environment may include thehierarchical multiplexer that facilitates successive multiplexing of aplurality of input data channels according to a configurable hierarchy.The hierarchy may be automatically configured by a controller based onan operational parameter in the industrial environment, such as aparameter of a machine in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors that may output data atdifferent rates. The system may also include a multiplexer module thatreceives sensor outputs from a first portion of the plurality of sensorswith similar output rates into separate inputs of a first hierarchicalmultiplexer of the multiplexer module. The first hierarchicalmultiplexer of the multiplexer module may provide at least onemultiplexed output of a portion of its inputs to a second hierarchicalmultiplexer that receives sensor outputs from a second portion of theplurality of sensors with similar output rates and that provides atleast one multiplexed output of a portion of its inputs. In embodiments,the output rates of the first set of sensors may be slower than theoutput rates of the second set of sensors. In embodiments, datacollection rate requirements of the first set of sensors may be lowerthan the data collection rate requirements of the second set of sensors.In embodiments, the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs. In embodiments,the second hierarchical multiplexer receives sensor signals with outputrates that are similar to a rate of output of the first multiplexer,wherein the first multiplexer produces time-based multiplexing of theportion of its plurality of inputs.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that is dynamicallyconfigured based on a data acquisition template. The hierarchicalmultiplexer may include a plurality of inputs and a plurality ofoutputs, wherein any input can be directed to any output in response tosensor output collection requirements of the template, and wherein asubset of the inputs can be multiplexed at a first switching rate andoutput to at least one of the plurality of outputs.

In embodiments, a system for data collection in an industrialenvironment may include a plurality of sensors for sensing conditions ofa machine in the environment, a hierarchical multiplexer, a plurality ofanalog-to-digital converters (ADCs), a processor, local storage, and anexternal interface. The system may use the processor to access a dataacquisition template of parameters for data collection from a portion ofthe plurality of sensors, configure the hierarchical multiplexer, theADCs and the local storage to facilitate data collection based on thedefined parameters, and execute the data collection with the configuredelements including storing a set of data collected from a portion of theplurality of sensors into the local storage. In embodiments, the ADCsconvert analog sensor data into a digital form that is compatible withthe hierarchical multiplexer. In embodiments, the processor monitors atleast one signal generated by the sensors for a trigger condition and,upon detection of the trigger condition, responds by at least one ofcommunicating an alert over the external interface and performing dataacquisition according to a template that corresponds to the triggercondition.

In embodiments, a system for data collection in an industrialenvironment may include a hierarchical multiplexer that may beconfigurable based on a data collection template of the environment. Themultiplexer may support receiving a large number of data signals (e.g.,from sensors in the environment) simultaneously. In embodiments, allsensors for a portion of an industrial machine in the environment may beindividually connected to inputs of a first stage of the multiplexer.The first stage of the multiplexer may provide a plurality of outputsthat may feed into a second multiplexer stage. The second stagemultiplexer may provide multiple outputs that feed into a third stage,and so on. Data collection templates for the environment may beconfigured for certain data collection sets, such as a set to determinetemperature throughout a machine or a set to determine vibrationthroughout a machine, and the like. Each template may identify aplurality of sensors in the environment from which data is to becollected, such as during a data collection event. When a template ispresented to the hierarchical multiplexer, mapping of inputs to outputsfor each multiplexing stage may be configured so that the required datais available at output(s) of a final multiplexing hierarchical stage fordata collection. In an example, a data collection template to collect aset of data to determine temperature throughout a machine in theenvironment may identify many temperature sensors. The first stagemultiplexer may respond to the template by selecting all of theavailable inputs that connect to temperature sensors. The data fromthese sensors maybe multiplexed onto multiple inputs of a second stagesensor that may perform time-based multiplexing to produce atime-multiplexed output(s) of temperature data from a portion of thesensors. These outputs may be gathered by a data collector andde-multiplexed into individual sensor temperature readings.

In embodiments, time-sensitive signals, such as triggers and the like,may connect to inputs that directly connect to a final multiplexerstage, thereby reducing any potential delay caused by routing throughmultiple multiplexing stages.

In embodiments, a hierarchical multiplexer in a system for datacollection in an industrial environment may comprise an array of relays,a programmable logic component, such as a CPLD, a field programmablegate array (FPGA), and the like.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with explosive systems inmining applications. Blast initiating and electronic blasting systemsmay be configured to provide computer assisted blasting systems.Ensuring that blasting occurs safely may involve effective sensing andanalysis of a range of conditions. A system for data collection in anindustrial environment may be deployed to sense and collect dataassociated with explosive systems, such as explosive systems used formining. A data collection system can use a hierarchical multiplexer tocapture data from explosive system installations automatically byaligning, for example, a deployment of the explosive system includingits layout plans, integration, interconnectivity, cascading plan, andthe like with the hierarchical multiplexer. An explosive system may bedeployed with a form of hierarchy that starts with a primary initiatorand follows detonation connections through successive layers ofelectronic blast control to sequenced detonation. Data collected fromeach of these layers of blast systems configuration may be associatedwith stages of a hierarchical multiplexer so that data collected frombulk explosive detonation can be captured in a hierarchy thatcorresponds to its blast control hierarchy.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with refinery blowers inoil and gas pipeline applications. Refinery blower applications includefired heater combustion air preheat systems and the like. Forced draftblowers may include a range of moving and moveable parts that maybenefit from condition sensing and monitoring. Sensing may includedetecting conditions of: couplings (e.g., temperature, rotational rate,and the like); motors (vibration, temperature, RPMs, torque, powerusage, and the like); louver mechanics (actuators, louvers, and thelike); and plenums (flow rate, blockage, back pressure, and the like). Asystem for data collection in an industrial environment that uses ahierarchical multiplexer for routing signals from sensors and the liketo data collectors may be configured to collect data from a refineryblower. In an example, a plurality of sensors may be deployed to senseair flow into, throughout, and out of a forced draft blower used in arefinery application, such as to preheat combustion air. Sensors may begrouped based on a frequency of a signal produced by sensors. Sensorsthat detect louver position and control may produce data at a lower ratethan sensors that detect blower RPMs. Therefore, louver position andcontrol sensor signals can be applied to a lower stage in a multiplexerhierarchy than the blower RPM sensors because data from louvers changeless often than data from RPM sensors. A data collection system couldswitch among a plurality of louver sensors and still capture enoughinformation to properly detect louver position. However, properlydetecting blower RPM data may require greater bandwidth of connectionbetween the blower RPM sensor and a data collector. A hierarchicalmultiplexer may enable capturing blower RPM data at a rate that isrequired for proper detection (perhaps by outputting the RPM sensor datafor long durations of time), while switching among several louver sensorinputs and directing them onto (or through) an output that is differentthan the blower RPM output. Alternatively, the louver inputs may betime-multiplexed with the blower RPM data onto a single output that canbe de-multiplexed by a data collector that is configured to determinewhen blower RPM data is being output and when louver position data isbeing output.

In embodiments, a system for data collection in an industrialenvironment that may include a hierarchical multiplexer for routingsensor outputs onto signal paths may be used with pipeline-relatedcompressors (e.g., reciprocating) in oil and gas pipeline applications.A typical use of a reciprocating compressor for pipeline application isproduction of compressed air for pipeline testing. A system for datacollection in an industrial environment may apply a hierarchicalmultiplexer while collecting data from a pipeline testing-basedreciprocating compressor. Data from sensors deployed along a portion ofa pipeline being tested may be input to the lowest stage of thehierarchical multiplexer because these sensors may be periodicallysampled prior to and during testing. However, the rate of sampling maybe low relative to sensors that detect compressor operation, such asparts of the compressor that operate at higher frequencies, such as thereciprocating linkage, motor, and the like. The sensors that providedata at frequencies that enable reproduction of the detected motion maybe input to higher stages in the hierarchical multiplexer. Timemultiplexing among the pipeline sensors may provide for coverage of alarge number of sensors while capturing events such as seal leakage andthe like. However, time multiplexing among reciprocating linkage sensorsmay require output signal bandwidth that may exceed the bandwidthavailable for routing data from the multiplexer to a data collector.Therefore, in embodiments, a plurality of pipeline sensors may betime-multiplexed onto a single multiplexer output and a compressorsensor detecting rapidly moving parts, such as the compressor motor, maybe routed to separate outputs of the multiplexer.

Referring to FIG. 18, a system for data collection in an industrialenvironment that uses a hierarchical multiplexer for routing sensorsignals to data collectors is depicted. Outputs from a plurality ofsensors, such as sensors that monitor conditions that change withrelatively low frequency (e.g., blower louver position sensors) may beinput to a lowest hierarchical stage 8000 of a hierarchical multiplexer8002 and routed to successively higher stages in the multiplexer,ultimately being output from the multiplexer, perhaps as atime-multiplexed signal comprising time-specific samples of each of theplurality of low frequency sensors. Outputs from a second plurality ofsensors, such as sensors that monitor motor operation that may run atmore than 1000 RPMs may be input to a higher hierarchical stage 8004 ofthe hierarchical multiplexer and routed to outputs that support therequired bandwidth.

An example system for data collection in an industrial environmentincludes a controller for controlling data collection resources in theindustrial environment and a hierarchical multiplexer that facilitatessuccessive multiplexing of a number of input data channels according toa configurable hierarchy, wherein the hierarchy is automaticallyconfigured by the controller based on an operational parameter of amachine in the industrial environment. In certain further embodiments,an example system includes: where the operational parameter of themachine is identified in a data collection template; where the hierarchyis automatically configured in response to smart band data collectionactivation further including an analog-to-digital converter disposedbetween a source of the input data channels and the hierarchicalmultiplexer; and/or where the operational parameter of the machinecomprises a trigger condition of at least one of the data channels.Another example system for data collection in an industrial environmentincludes a plurality of sensors and a multiplexer module that receivessensor outputs from a first portion of the sensors with similar outputrates into separate inputs of a first hierarchical multiplexer thatprovides at least one multiplexed output of a portion of its inputs to asecond hierarchical multiplexer, the second hierarchical multiplexerreceiving sensor outputs from a second portion of the sensors andproviding at least one multiplexed output of a portion of its inputs. Incertain further embodiments, an example system includes: where thesecond portion of the sensors output data at rates that are higher thanthe output rates of the first portion of the sensors; where the firstportion and the second portion of the sensors output data at differentrates; where the first hierarchical multiplexer output is atime-multiplexed combination of a portion of its inputs; where thesecond multiplexer receives sensor signals with output rates that aresimilar to a rate of output of the first multiplexer; and/or where thefirst multiplexer produces time-based multiplexing of the portion of itsinputs.

An example system for data collection in an industrial environmentincludes a number of sensors for sensing conditions of a machine in theenvironment a hierarchical multiplexer, a number of analog-to-digitalconverters, a controller, local storage, an external interface, wherethe system includes using the controller to access a data acquisitiontemplate that defines parameters for data collection from a portion ofthe sensors, to configure the hierarchical multiplexer, the ADCs, andthe local storage to facilitate data collection based on the definedparameters, and to execute the data collection with the configuredelements including storing a set of data collected from a portion of thesensors into the local storage. In certain further embodiments, anexample system includes: where the ADCs convert analog sensor data intoa digital form that is compatible with the hierarchical multiplexer;where the processor monitors at least one signal generated by thesensors for a trigger condition and, upon detection of the triggercondition, responds by communicating an alert over the externalinterface and/or performing data acquisition according to a templatethat corresponds to the trigger condition; where the hierarchicalmultiplexer performs successive multiplexing of data received from thesensors according to a configurable hierarchy; where the hierarchy isautomatically configured by the controller based on an operationalparameter of a machine in the industrial environment; where theoperational parameter of the machine is identified in a data collectiontemplate; where the hierarchy is automatically configured in response tosmart band data collection activation; the system further including anADC disposed between a source of the input data channels and thehierarchical multiplexer; where the operational parameter of the machineincludes a trigger condition of at least one of the data channels; wherethe hierarchical multiplexer performs successive multiplexing of datareceived from the plurality of sensors according to a configurablehierarchy; and/or where the hierarchy is automatically configured by acontroller based on a detected parameter of an industrial environment.Without limitation, n example system is configured for monitoring amining explosive system, and includes a controller for controlling datacollection resources associated with the explosive system, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the explosive system. Without limitation, anexample system is configured for monitoring a refinery blower in an oiland gas pipeline applications, and includes a controller for controllingdata collection resources associated with the refinery blower, and ahierarchical multiplexer that facilitates successive multiplexing of anumber of input data channels according to a configurable hierarchy,where the hierarchy is automatically configured by the controller basedon a configuration of the refinery blower. Without limitation, anexample system is configured for monitoring a reciprocating compressorin an oil and gas pipeline applications comprising, and includescontroller for controlling data collection resources associated with thereciprocating compressor, and a hierarchical multiplexer thatfacilitates successive multiplexing of a number of input data channelsaccording to a configurable hierarchy, where the hierarchy isautomatically configured by the controller based on a configuration ofthe reciprocating compressor.

Industrial components such as pumps, compressors, air conditioningunits, mixers, agitators, motors, and engines may play critical roles inthe operation of equipment in a variety of environments including aspart of manufacturing equipment in industrial environments such asfactories, gas handling systems, mining operations, automotive systems,and the like.

There are a wide variety of pumps such as a variety of positivedisplacement pumps, velocity pumps, and impulse pumps. Velocity orcentrifugal pumps typically comprise an impeller with curved bladeswhich, when an impeller is immersed in a fluid, such as water or a gas,causes the fluid or gas to rotate in the same rotational direction asthe impeller. As the fluid or gas rotates, centrifugal force causes itto move to the outer diameter of the pump, e.g., the pump housing, whereit can be collected and further processed. The removal of the fluid orgas from the outer circumference may result in lower pressure at a pumpinput orifice causing new fluid or gas to be drawn into the pump.

Positive displacement pumps may comprise reciprocating pumps,progressive cavity pumps, gear or screw pumps, such as reciprocatingpumps typically comprise a piston which alternately creates suction,which opens an inlet valve and draws a liquid or gas into a cylinder,and pressure, which closes the inlet valve and forces the liquid or gaspresent out of the cylinder through an outlet valve. This method ofpumping may result in periodic waves of pressurized liquid or gas beingintroduced into the downstream system.

Some automotive vehicles such as cars and trucks may use a water coolingsystem to keep the engine from overheating. In some automobiles, acentrifugal water pump, driven by a belt associated with a driveshaft ofthe vehicle, is used to force a mixture of water and coolant through theengine to maintain an acceptable engine temperature. Overheating of theengine may be highly destructive to the engine and yet it may bedifficult or costly to access a water pump installed in a vehicle.

In embodiments, a vehicle water pump may be equipped with a plurality ofsensors for measuring attributes associated with the water pump such astemperature of bearings or pump housing, vibration of a driveshaftassociated with the pump, liquid leakage, and the like. These sensorsmay be connected either directly to a monitoring device or through anintermediary device using a mix of wired and wireless connectiontechniques. A monitoring device may have access to detection valuescorresponding to the sensors where the detection values corresponddirectly to the sensor output or a processed version of the data outputsuch as a digitized or sampled version of the sensor output, and/or avirtual sensor or modeled value correlated from other sensed values. Themonitoring device may access and process the detection values usingmethods discussed elsewhere herein to evaluate the health of the waterpump and various components of the water pump prone to wear and failure,e.g., bearings or sets of bearings, drive shafts, motors, and the like.The monitoring device may process the detection values to identify atorsion of the drive shaft of the pump. The identified torsion may thenbe evaluated relative to expected torsion based on the specific geometryof the water pump and how it is installed in the vehicle. Unexpectedtorsion may put undue stress on the driveshaft and may be a sign ofdeteriorating health of the pump. The monitoring device may process thedetection values to identify unexpected vibrations in the shaft orunexpected temperature values or temperature changes in the bearings orin the housing in proximity to the bearings. In some embodiments, thesensors may include multiple temperature sensors positioned around thewater pump to identify hot spots among the bearings or across the pumphousing which might indicate potential bearing failure. The monitoringdevice may process the detection values associated with water sensors toidentify liquid leakage near the pump which may indicate a bad seal. Thedetection values may be jointly analyzed to provide insight into thehealth of the pump.

In an illustrative example, detection values associated with a vehiclewater pump may show a sudden increase in vibration at a higher frequencythan the operational rotation of the pump with a corresponding localizedincrease of temperature associated with a specific phase in the pumpcycle. Together these may indicate a localized bearing failure.

Production lines may also include one or more pumps for moving a varietyof material including acidic or corrosive materials, flammablematerials, minerals, fluids comprising particulates of varying sizes,high viscosity fluids, variable viscosity fluids, or high-densityfluids. Production line pumps may be designed to specifically meet theneeds of the production line including pump composition to handle thevarious material types, or torque needed to move the fluid at thedesired speed or with the desired pressure. Because these productionlines may be continuous process lines, it may be desirable to performproactive maintenance rather than wait for a component to fail.Variations in pump speed and pressure may have the potential tonegatively impact the final product, and the ability to identify issuesin the final product may lag the actual component deterioration by anunacceptably long period.

In embodiments, an industrial pump may be equipped with a plurality ofsensors for measuring attributes associated with the pump such astemperature of bearings or pump housing, vibration of a driveshaftassociated with the pump, vibration of input or output lines, pressure,flow rate, fluid particulate measures, vibrations of the pump housing,and the like. These sensors may be connected either directly to amonitoring device or through an intermediary device using a mix of wiredand wireless connection techniques. A monitoring device may have accessto detection values corresponding to the sensors where the detectionvalues correspond directly to the sensor output of a processed versionof the data output such as a digitized or sampled version of the sensoroutput. The monitoring device may access and process the detectionvalues using methods discussed elsewhere herein to evaluate the healthof the pump overall, evaluate the health of pump components, predictpotential down line issues arising from atypical pump performance, orchanges in fluid being pumped. The monitoring device may process thedetection values to identify torsion on the drive shaft of the pump. Theidentified torsion may then be evaluated relative to expected torsionbased on the specific geometry of the pump and how it is installed inthe equipment relative to other components on the assembly line.Unexpected torsion may put undue stress on the driveshaft and may be asign of deteriorating health of the pump. Vibration of the inlet andoutlet pipes may also be evaluated for unexpected or resonant vibrationswhich may be used to drive process controls to avoid certain pumpfrequencies. Changes in vibration may also be due to changes in fluidcomposition or density, amplifying or dampening vibrations at certainfrequencies. The monitoring device may process the detection values toidentify unexpected vibrations in the shaft, unexpected temperaturevalues, or temperature changes in the bearings or in the housing inproximity to the bearings. In some embodiments, the sensors may includemultiple temperature sensors positioned around the pump to identify hotspots among the bearings or across the pump housing which mightindicated potential bearing failure. For some pumps, when the fluidbeing pumped is corrosive or contains large amounts of particulates,there may be damage to the interior components of the pump in contactwith the fluid due to cumulative exposure to the fluid. This may bereflected in unanticipated variations in output pressure. Additionallyor alternatively, if a gear in a gear pump begins to corrode and nolonger forces all the trapped fluid out this may result in increasedpump speed, fluid cavitation, and/or unexpected vibrations in the outputpipe.

Compressors increase the pressure of a gas by decreasing the volumeoccupied by the gas or increasing the amount of the gas in a confinedvolume. There may be positive-displacement compressors that utilize themotion of pistons or rotary screws to move the gas into a pressurizedholding chamber. There are dynamic displacement gas compressors that usecentrifugal force to accelerate the gas into a stationary compressorwhere the kinetic energy is converted to pressure. Compressors may beused to compress various gases for use on an assembly line. Compressedair may power pneumatic equipment on an assembly line. In the oil andgas industry, flash gas compressors may be used to compress gas so thatit leaves a hydrocarbon liquid when it enters a lower pressureenvironment. Compressors may be used to restore pressure in gas and oilpipelines, to mix fluids of interest, and/or to transfer or transportfluids of interest. Compressors may be used to enable the undergroundstorage of natural gas.

Like pumps, compressors may be equipped with a plurality of sensors formeasuring attributes associated with the compressor such as temperatureof bearings or compressor housing, vibration of a driveshaft,transmission, gear box and the like associated with the compressor,vessel pressure, flow rate, and the like. These sensors may be connectedeither directly to a monitoring device or through an intermediary deviceusing a mix of wired and wireless connection techniques. A monitoringdevice may have access to detection values corresponding to the sensorswhere the detection values correspond directly to the sensor output of aprocessed version of the data output such as a digitized or sampledversion of the sensor output. The monitoring device may access andprocess the detection values using methods described elsewhere herein toevaluate the health of the compressor overall, evaluate the health ofcompressor components and/or predict potential down line issues arisingfrom atypical compressor performance. The monitoring device may processthe detection values to identify torsion on a driveshaft of thecompressor. The identified torsion may then be evaluated relative toexpected torsion based on the specific geometry of the compressor andhow it is installed in the equipment relative to other components andpieces of equipment. Unexpected torsion may put undue stress on thedriveshaft and may be a sign of deteriorating health of the compressor.Vibration of the inlet and outlet pipes may also be evaluated forunexpected or resonant vibrations which may be used to drive processcontrols to avoid certain compressor frequencies. The monitoring devicemay process the detection values to identify unexpected vibrations inthe shaft, unexpected temperature values or temperature changes in thebearings or in the housing in proximity to the bearings. In someembodiments, the sensors may include multiple temperature sensorspositioned around the compressor to identify hot spots among thebearings or across the compressor housing, which might indicatepotential bearing failure. In some embodiments, sensors may monitor thepressure in a vessel storing the compressed gas. Changes in the pressureor rate of pressure change may be indicative of problems with thecompressor.

Agitators and mixers are used in a variety of industrial environments.Agitators may be used to mix together different components such asliquids, solids, or gases. Agitators may be used to promote a morehomogenous mixture of component materials. Agitators may be used topromote a chemical reaction by increasing exposure between differentcomponent materials and adding energy to the system. Agitators may beused to promote heat transfer to facilitate uniform heating or coolingof a material.

Mixers and agitators are used in such diverse industries as chemicalproduction, food production, pharmaceutical production, and the like.There are paint and coating mixers, adhesive and sealant mixers, oil andgas mixers, water treatment mixers, wastewater treatment mixers, and thelike.

Agitators may comprise equipment that rotates or agitates an entire tankor vessel in which the materials to be mixed are located, such as aconcrete mixer. Effective agitations may be influenced by the number andshape of baffles in the interior of the tank. Agitation by rotation ofthe tank or vessel may be influenced by the axis of rotation relative tothe shape of the tank, direction of rotation, and external forces suchas gravity acting on the material in the tank. Factors affecting theefficacy of material agitation or mixing by agitation of the tank orvessel may include axes of rotation, and amplitude and frequency ofvibration along different axes. These factors may be selected based onthe types of materials being selected, their relative viscosities,specific gravities, particulate count, any shear thinning or shearthickening anticipated for the component materials or mixture, flowrates of material entering or exiting the vessel or tank, direction andlocation of flows of material entering of exiting the vessel, and thelike.

Agitators, large tank mixers, portable tank mixers, tote tank mixers,drum mixers, and mounted mixers (with various mount types) may comprisea propeller or other mechanical device such as a blade, vane, or statorinserted into a tank of materials to be mixed, while rotating apropeller or otherwise moving a mechanical device. These may includeairfoil impellers, fixed pitch blade impellers, variable pitch bladeimpellers, anti-ragging impellers, fixed radial blade impellers,marine-type propellers, collapsible airfoil impellers, collapsiblepitched blade impellers, collapsible radial blade impellers, andvariable pitch impellers. Agitators may be mounted such that themechanical agitation is centered in the tank. Agitators may be mountedsuch that they are angled in a tank or are vertically or horizontallyoffset from the center of the vessel. The agitators may enter the tankfrom above, below, or the side of the tank. There may be a plurality ofagitators in a single tank to achieve uniform mixing throughout the tankor container of chemicals.

Agitators may include the strategic flow or introduction of componentmaterials into the vessel including the location and direction of entry,rate of entry, pressure of entry, viscosity of material, specificgravity of the material, and the like.

Successful agitation of mixing of materials may occur with a combinationof techniques such as one or more propellers in a baffled tank wherecomponents are being introduced at different locations and at differentrates.

In embodiments, an industrial mixer or agitator may be equipped with aplurality of sensors for measuring attributes associated with theindustrial mixer such as: temperature of bearings or tank housing,vibration of driveshafts associated with a propeller or other mechanicaldevice such as a blade, vane or stator, vibration of input or outputlines, pressure, flow rate, fluid particulate measures, vibrations ofthe tank housing and the like. These sensors may be connected eitherdirectly to a monitoring device or through an intermediary device usinga mix of wired and wireless connection techniques. A monitoring devicemay have access to detection values corresponding to the sensors wherethe detection values correspond directly to the sensor output of aprocessed version of the data, output such as a digitized or sampledversion of the sensor output, fusion of data from multiple sensors, andthe like. The monitoring device may access and process the detectionvalues using methods discussed elsewhere herein to evaluate the healthof the agitator or mixer overall, evaluate the health of agitator ormixer components, predict potential down line issues arising fromatypical performance or changes in composition of material beingagitated. For example, the monitoring device may process the detectionvalues to identify torsion on the driveshaft of an agitating impeller.The identified torsion may then be evaluated relative to expectedtorsion based on the specific geometry of the agitator and how it isinstalled in the equipment relative to other components and/or pieces ofequipment. Unexpected torsion may put undue stress on the driveshaft andmay be a sign of deteriorating health of the agitator. Vibration ofinflow and outflow pipes may be monitored for unexpected or resonantvibrations which may be used to drive process controls to avoid certainagitation frequencies. Inflow and outflow pipes may also be monitoredfor unexpected flow rates, unexpected particulate content, and the like.Changes in vibration may also be due to changes in fluid composition, ordensity amplifying or dampening vibrations at certain frequencies. Themonitoring device may distribute sensors to collect detection valueswhich may be used to identify unexpected vibrations in the shaft, orunexpected temperature values or temperature changes in the bearings orin the housing in proximity to the bearings. For some agitators, whenthe fluid being agitated is corrosive or contains large amounts ofparticulates, there may be damage to the interior components of theagitator (e.g., baffles, propellers, blades, and the like) which are incontact with the materials, due to cumulative exposure to the materials.

HVAC, air-conditioning systems, and the like may use a combination ofcompressors and fans to cool and circulate air in industrialenvironments. Similar to the discussion of compressors and agitators,these systems may include a number of rotating components whose failureor reduced performance might negatively impact the working environmentand potentially degrade product quality. A monitoring device may be usedto monitor sensors measuring various aspects of the one or more rotatingcomponents, the venting system, environmental conditions, and the like.Components of the HVAC/air-conditioning systems may include fan motors,driveshafts, bearings, compressors, and the like. The monitoring devicemay access and process the detection values corresponding to the sensoroutputs according to methods discussed elsewhere herein to evaluate theoverall health of the air-conditioning unit, HVAC system, and like aswell as components of these systems, identify operational states,predict potential issues arising from atypical performance, and thelike. Evaluation techniques may include bearing analysis, torsionalanalysis of driveshafts, rotors and stators, peak value detection, andthe like. The monitoring device may process the detection values toidentify issues such as torsion on a driveshaft, potential bearingfailures, and the like.

Assembly line conveyors may comprise a number of moving and rotatingcomponents as part of a system for moving material through amanufacturing process. These assembly line conveyors may operate over awide range of speeds. These conveyances may also vibrate at a variety offrequencies as they convey material horizontally to facilitatescreening, grading, laning for packaging, spreading, dewatering, feedingproduct into the next in-line process, and the like.

Conveyance systems may include engines or motors, one or moredriveshafts turning rollers or bearings along which a conveyor belt maymove. A vibrating conveyor may include springs and a plurality ofvibrators which vibrate the conveyor forward in a sinusoidal manner.

In embodiments, conveyors and vibrating conveyors may be equipped with aplurality of sensors for measuring attributes associated with theconveyor such as temperature of bearings, vibration of driveshafts,vibrations of rollers along which the conveyor travels, velocity andspeed associated with the conveyor, and the like. The monitoring devicemay access and process the detection values using methods discussedelsewhere herein to evaluate the overall health of the conveyor as wellas components of the conveyor, predict potential issues arising fromatypical performance, and the like. Techniques for evaluating theconveyors may include bearing analysis, torsional analysis, phasedetection/phase lock loops to align detection values from differentparts of the conveyor, frequency transformations and frequency analysis,peak value detection, and the like. The monitoring device may processthe detection values to identify torsion on a driveshaft, potentialbearing failures, uneven conveyance and like.

In an illustrative example, a paper-mill conveyance system may comprisea mesh onto which the paper slurry is coated. The mesh transports theslurry as liquid evaporates and the paper dries. The paper may then bewound onto a core until the roll reaches diameters of up to threemeters. The transport speeds of the paper-mill range from traditionalequipment operating at 14-48 meters/minute to new, high-speed equipmentoperating at close to 2000 meters/minute. For slower machines, the papermay be winding onto the roll at 14 meters/minute which, towards the endof the roll having a diameter of approximately three meters wouldindicate that the take up roll may be rotating at speeds on the order ofa couple of rotations a minute. Vibrations in the web conveyance ortorsion across the take up roller may result in damage to the paper,skewing of the paper on the web, or skewed rolls which may result inequipment downtime or product that is lower in quality or unusable.Additionally, equipment failure may result in costly machine shutdownsand loss of product. Therefore, the ability to predict problems andprovide preventative maintenance and the like may be useful.

Monitoring truck engines and steering systems to facilitate timelymaintenance and avoid unexpected breakdowns may be important. Health ofthe combustion chamber, rotating crankshafts, bearings, and the like maybe monitored using a monitoring device structured to interpret detectionvalues received from a plurality of sensors measuring a variety ofcharacteristics associated with engine components including temperature,torsion, vibration, and the like. As discussed above, the monitoringdevice may process the detection values to identify engine bearinghealth, torsional vibrations on a crankshaft/driveshaft, unexpectedvibrations in the combustion chambers, overheating of differentcomponents, and the like. Processing may be done locally or data may becollected across a number of vehicles and jointly analyzed. Themonitoring device may process detection values associated with theengine, combustion chambers, and the like. Sensors may monitortemperature, vibration, torsion, acoustics, and the like to identifyissues. A monitoring device or system may use techniques such as peakdetection, bearing analysis, torsion analysis, phase detection, PLL,band pass filtering, and the like to identify potential issues with thesteering system and bearing and torsion analysis to identify potentialissues with rotating components on the engine. This identification ofpotential issues may be used to schedule timely maintenance, reduceoperation prior to maintenance, and influence future component design.

Drilling machines and screwdrivers in the oil and gas industries may besubjected to significant stresses. Because they are frequently situatedin remote locations, an unexpected breakdown may result in extended downtime due to the lead-time associated with bringing in replacementcomponents. The health of a drilling machine or screwdriver andassociated rotating crankshafts, bearings, and the like may be monitoredusing a monitoring device structured to interpret detection valuesreceived from a plurality of sensors measuring a variety ofcharacteristics associated with the drilling machine or screwdriverincluding temperature, torsion, vibration, rotational speed, verticalspeed, acceleration, image sensors, and the like. As discussed above,the monitoring device may process the detection values to identifyequipment health, torsional vibrations on a crankshaft/driveshaft,unexpected vibrations in the component, overheating of differentcomponents, and the like. Processing may be done locally or datacollected across a number of machines and jointly analyzed. Themonitoring device may jointly process detection values, equipmentmaintenance records, product records, historical data, and the like toidentify correlations between detection values, current and futurestates of the component, anticipated lifetime of the component or pieceof equipment, and the like. Sensors may monitor temperature, vibration,torsion, acoustics, and the like to identify issues such asunanticipated torsion in the drill shaft, slippage in the gears,overheating, and the like. A monitoring device or system may usetechniques such as peak detection, bearing analysis, torsion analysis,phase detection, PLL, band pass filtering, and the like to identifypotential issues. This identification of potential issues may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance, and influence future component design.

Similarly, it may be desirable to monitor the health of gearboxesoperating in an oil and gas field. A monitoring device may be structuredto interpret detection values received from a plurality of sensorsmeasuring a variety of characteristics associated with the gearbox suchas temperature, vibration, and the like. The monitoring device mayprocess the detection values to identify gear and gearbox health andanticipated life. Processing may be done locally or data collectedacross a number of gearboxes and jointly analyzed. The monitoring devicemay jointly process detection values, equipment maintenance records,product records historical data, and the like to identify correlationsbetween detection values, current and future states of the gearbox,anticipated lifetime of the gearbox and associated components, and thelike. A monitoring device or system may use techniques such as peakdetection, bearing analysis, torsion analysis, phase detection, PLL,band pass filtering, to identify potential issues. This identificationof potential issues may be used to schedule timely maintenance, ordernew or replacement components, reduce operation prior to maintenance,and influence future equipment design.

Refining tanks in the oil and gas industries may be subjected tosignificant stresses due to the chemical reactions occurring inside.Because a breach in a tank could result in the release of potentiallytoxic chemicals, it may be beneficial to monitor the condition of therefining tank and associated components. Monitoring a refining tank tocollect a variety of ongoing data may be used to predict equipment wear,component wear, unexpected stress, and the like. Given predictions aboutequipment health, such as the status of a refining tank, may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance, and influence future component design.Similar to the discussion above, a refining tank may be monitored usinga monitoring device structured to interpret detection values receivedfrom a plurality of sensors measuring a variety of characteristicsassociated with the refining tank such as temperature, vibration,internal and external pressure, the presence of liquid or gas at seamsand ports, and the like. The monitoring device may process the detectionvalues to identify equipment health, unexpected vibrations in the tank,overheating of the tank or uneven heating across the tank, and the like.Processing may be done locally or data collected across a number oftanks and jointly analyzed. The monitoring device may jointly processdetection values, equipment maintenance records, product recordshistorical data, and the like to identify correlations between detectionvalues, current and future states of the tank, anticipated lifetime ofthe tank and associated components, and the like. A monitoring device orsystem may use techniques such as peak detection, bearing analysis,torsion analysis, phase detection, PLL, band pass filtering, and thelike to identify potential issues.

Similarly, it may be desirable to monitor the health of centrifugesoperating in an oil and gas refinery. A monitoring device may bestructured to interpret detection values received from a plurality ofsensors measuring a variety of characteristics associated with thecentrifuge such as temperature, vibration, pressure, and the like. Themonitoring device may process the detection values to identify equipmenthealth, unexpected vibrations in the centrifuge, overheating, pressureacross the centrifuge, and the like. Processing may be done locally ordata collected across a number of centrifuges and jointly analyzed. Themonitoring device may jointly process detection values, equipmentmaintenance records, product records historical data, and the like toidentify correlations between detection values, current and futurestates of the centrifuge, anticipated lifetime of the centrifuge andassociated components, and the like. A monitoring device or system mayuse techniques such as peak detection, bearing analysis, torsionanalysis, phase detection, PLL, band pass filtering, to identifypotential issues. This identification of potential issues may be used toschedule timely maintenance, order new or replacement components, reduceoperation prior to maintenance and influence future equipment design.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement, and the like. An embodiment of a datamonitoring device 8100 is shown in FIG. 19 and may include a pluralityof sensors 8106 communicatively coupled to a controller 8102. Thecontroller 8102 may include a data acquisition circuit 8104, a dataanalysis circuit 8108, a MUX control circuit 8114, and a responsecircuit 8110. The data acquisition circuit 8104 may include a MUX 8112where the inputs correspond to a subset of the detection values. The MUXcontrol circuit 8114 may be structured to provide adaptive scheduling ofthe logical control of the MUX and the correspondence of MUX input anddetected values based on a subset of the plurality of detection valuesand/or a command from the response circuit 8110 and/or the output of thedata analysis circuit 8108. The data analysis circuit 8108 may compriseone or more of a peak detection circuit, a phase differential circuit, aPLL circuit, a bandpass filter circuit, a frequency transformationcircuit, a frequency analysis circuit, a torsional analysis circuit, abearing analysis circuit, an overload detection circuit, a sensor faultdetection circuit, a vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,overloading of weight, excessive forces, stress and strain-basedeffects, and the like. The data analysis circuit 8108 may output acomponent health status as a result of the analysis.

The data analysis circuit 8108 may determine a state, condition, orstatus of a component, part, sub-system, or the like of a machine,device, system or item of equipment (collectively referred to herein asa component health status) based on a maximum value of a MUX output fora given input or a rate of change of the value of a MUX output for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on a time integration of the value of a MUX for agiven input. The data analysis circuit 8108 may determine a componenthealth status based on phase differential of MUX output relative to anon-board time or another sensor. The data analysis circuit 8108 maydetermine a component health status based on a relationship of value,phase, phase differential, and rate of change for MUX outputscorresponding to one or more input detection values. The data analysiscircuit 8108 may determine a component health status based on processstage or component specification or component anticipated state.

The multiplexer control circuit 8114 may adapt the scheduling of thelogical control of the multiplexer based on a component health status,an anticipated component health status, the type of component, the typeof equipment being measured, an anticipated state of the equipment, aprocess stage (different parameters/sensor values) may be important atdifferent stages in a process. The multiplexer control circuit 8114 mayadapt the scheduling of the logical control of the multiplexer based ona sequence selected by a user or a remote monitoring application, or onthe basis of a user request for a specific value. The multiplexercontrol circuit 8114 may adapt the scheduling of the logical control ofthe multiplexer based on the basis of a storage profile or plan (such asbased on type and availability of storage elements and parameters asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), network conditions or availability (also asdescribed elsewhere in this disclosure and in the documents incorporatedherein by reference), or value or cost of component or equipment.

The plurality of sensors 8106 may be wired to ports on the dataacquisition circuit 8104. The plurality of sensors 8106 may bewirelessly connected to the data acquisition circuit 8104. The dataacquisition circuit 8104 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8106 where the sensors 8106 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8106 for a data monitoringdevice 8100 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors, andthe like. The impact of a failure, time response of a failure (e.g.,warning time and/or off-nominal modes occurring before failure),likelihood of failure, and/or sensitivity required, and/or difficulty todetect failure conditions may drive the extent to which a component orpiece of equipment is monitored with more sensors, and/or highercapability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating, and the like, sensors8106 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor and/or a currentsensor (for the component and/or other sensors measuring the component),an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition, and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, a thermal imager, an acoustic wavesensor, a displacement sensor, a turbidity meter, a viscosity meter, anaxial load sensor, a radial load sensor, a tri-axial sensor, anaccelerometer, a speedometer, a tachometer, a fluid pressure meter, anair flow meter, a horsepower meter, a flow rate meter, a fluid particledetector, an optical (laser) particle counter, an ultrasonic sensor, anacoustical sensor, a heat flux sensor, a galvanic sensor, amagnetometer, a pH sensor, and the like, including, without limitation,any of the sensors described throughout this disclosure and thedocuments incorporated by reference.

The sensors 8106 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8106 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8106 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

The sensors 8106 may monitor components such as bearings, sets ofbearings, motors, driveshafts, pistons, pumps, conveyors, vibratingconveyors, compressors, drills, and the like in vehicles, oil and gasequipment in the field, in assembly line components, and the like.

In embodiments, as illustrated in FIG. 19, the sensors 8106 may be partof the data monitoring device 8100, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 20 and 21, oneor more external sensors 8126, which are not explicitly part of amonitoring device 8120 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to, or accessed by the monitoring device 8120. The monitoringdevice 8120 may include a controller 8122. The controller 8122 mayinclude a data acquisition circuit 8104, a data analysis circuit 8108, aMUX control circuit 8114, and a response circuit 8110. The dataacquisition circuit 8104 may comprise a MUX 8112 where the inputscorrespond to a subset of the detection values. The MUX control circuit8114 may be structured to provide the logical control of the MUX and thecorrespondence of MUX input and detected values based on a subset of theplurality of detection values and/or a command from the response circuit8110 and/or the output of the data analysis circuit 8108. The dataanalysis circuit 8108 may comprise one or more of a peak detectioncircuit, a phase differential circuit, a PLL circuit, a bandpass filtercircuit, a frequency transformation circuit, a frequency analysiscircuit, a torsional analysis circuit, a bearing analysis circuit, anoverload detection circuit, vibrational resonance circuit for theidentification of unfavorable interaction among machines or components,a distortion identification circuit for the identification ofunfavorable distortions such as deflections shapes upon operation,stress and strain-based effects, and the like.

The one or more external sensors 8126 may be directly connected to theone or more input ports 8128 on the data acquisition circuit 8104 of thecontroller 8122 or may be accessed by the data acquisition circuit 8104wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, as shown in FIG. 23, a data acquisition circuit 8104 mayfurther comprise a wireless communication circuit 8130. The dataacquisition circuit 8104 may use the wireless communication circuit 8130to access detection values corresponding to the one or more externalsensors 8126 wirelessly or via a separate source or some combination ofthese methods.

In embodiments, as illustrated in FIG. 22, the controller 8134 mayfurther comprise a data storage circuit 8136. The data storage circuit8136 may be structured to store one or more of sensor specifications,component specifications, anticipated state information, detectedvalues, multiplexer output, component models, and the like. The datastorage circuit 8136 may provide specifications and anticipated stateinformation to the data analysis circuit 8108.

In embodiments, the response circuit 8110 may initiate a variety ofactions based on the sensor status provided by the data analysis circuit8108. The response circuit 8110 may adjust a sensor scaling value (e.g.,from 100 mV/gram to 10 mV/gram). The response circuit 8110 may select analternate sensor from a plurality available. The response circuit 8110may acquire data from a plurality of sensors of different ranges. Theresponse circuit 8110 may recommend an alternate sensor. The responsecircuit 8110 may issue an alarm or an alert.

In embodiments, the response circuit 8110 may cause the data acquisitioncircuit 8104 to enable or disable the processing of detection valuescorresponding to certain sensors based on the component status. This mayinclude switching to sensors having different response rates,sensitivity, ranges, and the like; accessing new sensors or types ofsensors, accessing data from multiple sensors, and the like. Switchingmay be undertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available, such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection, or to a location where different sensors can be accessed,such as moving a collector to connect up to a sensor at a location in anenvironment by a wired or wireless connection. This switching may beimplemented by directing changes to the multiplexer (MUX) controlcircuit 8114.

In embodiments, the response circuit 8110 may make recommendations forthe replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8110 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8110 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range, and the like. In embodiments, the response circuit8110 may implement or recommend process changes—for example to lower theutilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but is still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, the data analysis circuit 8108 and/or the responsecircuit 8110 may periodically store certain detection values and/or theoutput of the multiplexers and/or the data corresponding to the logiccontrol of the MUX in the data storage circuit 8136 to enable thetracking of component performance over time. In embodiments, based onsensor status, as described elsewhere herein, recently measured sensordata and related operating conditions such as RPMs, component loads,temperatures, pressures, vibrations, or other sensor data of the typesdescribed throughout this disclosure in the data storage circuit 8136enable the backing out of overloaded/failed sensor data. The dataanalysis circuit 8108 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIGS. 23, 24, 25, and 26, a data monitoringsystem 8138 may include at least one data monitoring device 8140. The atleast one data monitoring device 8140 may include sensors 8106 and acontroller 8142 comprising a data acquisition circuit 8104, a dataanalysis circuit 8108, a data storage circuit 8136, and a communicationcircuit 8146 to allow data and analysis to be transmitted to amonitoring application 8150 on a remote server 8148. The signalevaluation circuit 8108 may include at least an overload detectioncircuit (e.g., reference FIGS. 69 and 70) and/or a sensor faultdetection circuit (e.g., reference FIGS. 69 and 70). The signalevaluation circuit 8108 may periodically share data with thecommunication circuit 8146 for transmittal to the remote server 8148 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 8150. Based on thesensor status, the signal evaluation circuit 8108 and/or responsecircuit 8110 may share data with the communication circuit 8146 fortransmittal to the remote server 8148 based on the fit of data relativeto one or more criteria. Data may include recent sensor data andadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8108 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as shown in FIG. 23, the communication circuit 8146 maycommunicate data directly to a remote server 8148. In embodiments, asshown in FIG. 24, the communication circuit 8146 may communicate data toan intermediate computer 8152 which may include a processor 8154 runningan operating system 8156 and a data storage circuit 8158.

In embodiments as illustrated in FIGS. 25 and 26, a data collectionsystem 8160 may have a plurality of monitoring devices 8144 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility,as well as collecting data from monitoring devices in multiplefacilities. A monitoring application 8150 on a remote server 8148 mayreceive and store one or more of detection values, timing signals, anddata coming from a plurality of the various monitoring devices 8144.

In embodiments, as shown in FIG. 25, the communication circuit 8146 maycommunicate data directly to a remote server 8148. In embodiments, asshown in FIG. 26, the communication circuit 8146 may communicate data toan intermediate computer 8152 which may include a processor 8154 runningan operating system 8156 and a data storage circuit 8158. There may bean individual intermediate computer 8152 associated with each monitoringdevice 8140 or an individual intermediate computer 8152 may beassociated with a plurality of monitoring devices 8144 where theintermediate computer 8152 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8148. Communication to the remote server 8148 may be streaming, batch(e.g., when a connection is available), or opportunistic.

The monitoring application 8150 may select subsets of the detectionvalues to be jointly analyzed. Subsets for analysis may be selectedbased on a single type of sensor, component, or a single type ofequipment in which a component is operating. Subsets for analysis may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent or continuous),operating speed or tachometer output, common ambient environmentalconditions such as humidity, temperature, air or fluid particulate, andthe like. Subsets for analysis may be selected based on the effects ofother nearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

In embodiments, the monitoring application 8150 may analyze the selectedsubset. In an example, data from a single sensor may be analyzed overdifferent time periods such as one operating cycle, several operatingcycles, a month, a year, the life of the component, or the like. Datafrom multiple sensors of a common type measuring a common component typemay also be analyzed over different time periods. Trends in the datasuch as changing rates of change associated with start-up or differentpoints in the process may be identified. Correlation of trends andvalues for different sensors may be analyzed to identify thoseparameters whose short-term analysis might provide the best predictionregarding expected sensor performance. This information may betransmitted back to the monitoring device to update sensor models,sensor selection, sensor range, sensor scaling, sensor samplingfrequency, types of data collected, and the like, and be analyzedlocally or to influence the design of future monitoring devices.

In embodiments, the monitoring application 8150 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofsensors, operational history, historical detection values, sensor lifemodels, and the like for use analyzing the selected subset usingrule-based or model-based analysis. The monitoring application 8150 mayprovide recommendations regarding sensor selection, additional data tocollect, data to store with sensor data, and the like. The monitoringapplication 8150 may provide recommendations regarding schedulingrepairs and/or maintenance. The monitoring application 8150 may providerecommendations regarding replacing a sensor. The replacement sensor maymatch the sensor being replaced or the replacement sensor may have adifferent range, sensitivity, sampling frequency, and the like.

In embodiments, the monitoring application 8150 may include a remotelearning circuit structured to analyze sensor status data (e.g., sensoroverload or sensor failure) together with data from other sensors,failure data on components being monitored, equipment being monitored,output being produced, and the like. The remote learning system mayidentify correlations between sensor overload and data from othersensors.

An example monitoring system for data collection in an industrialenvironment includes a data acquisition circuit that interprets a numberof detection values, each of the detection values corresponding to inputreceived from at least one of a number of input sensors, a MUX havinginputs corresponding to a subset of the detection values, a MUX controlcircuit that interprets a subset of the number of detection values andprovides the logical control of the MUX and the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, a data analysiscircuit that receives an output from the MUX and data corresponding tothe logic control of the MUX resulting in a component health status, ananalysis response circuit that performs an operation in response to thecomponent health status, where the number of sensors includes at leasttwo sensors such as a temperature sensor, a load sensor, a vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor, and/or a tachometer. Incertain further embodiments, an example system includes: where at leastone of the number of detection values may correspond to a fusion of twoor more input sensors representing a virtual sensor; where the systemfurther includes a data storage circuit that stores at least one ofcomponent specifications and anticipated component state information andbuffers a subset of the number of detection values for a predeterminedlength of time; where the system further includes a data storage circuitthat stores at least one of a component specification and anticipatedcomponent state information and buffers the output of the MUX and datacorresponding to the logic control of the MUX for a predetermined lengthof time; where the data analysis circuit includes a peak detectioncircuit, a phase detection circuit, a bandpass filter circuit, afrequency transformation circuit, a frequency analysis circuit, a PLLcircuit, a torsional analysis circuit, and/or a bearing analysiscircuit; where operation further includes storing additional data in thedata storage circuit; where the operation includes at least one ofenabling or disabling one or more portions of the MUX circuit; and/orwhere the operation includes causing the MUX control circuit to alterthe logical control of the MUX and the correspondence of MUX input anddetected values. In certain embodiments, the system includes at leasttwo multiplexers; control of the correspondence of the multiplexer inputand the detected values further includes controlling the connection ofthe output of a first multiplexer to an input of a second multiplexer;control of the correspondence of the multiplexer input and the detectedvalues further comprises powering down at least a portion of one of theat least two multiplexers; and/or control of the correspondence of MUXinput and detected values includes adaptive scheduling of the selectlines. In certain embodiments, a data response circuit analyzes thestream of data from one or both MUXes, and recommends an action inresponse to the analysis.

An example testing system includes the testing system in communicationwith a number of analog and digital input sensors, a monitoring deviceincluding a data acquisition circuit that interprets a number ofdetection values, each of the number of detection values correspondingto at least one of the input sensors, a MUX having inputs correspondingto a subset of the detection values, a MUX control circuit thatinterprets a subset of the number of detection values and provides thelogical control of the MUX and control of the correspondence of MUXinput and detected values as a result, where the logic control of theMUX includes adaptive scheduling of the select lines, and a userinterface enabled to accept scheduling input for select lines anddisplay output of MUX and select line data.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by looking at both the amplitude and phase or timing ofdata signals relative to related data signals, timers, reference signalsor data measurements. An embodiment of a data monitoring device 8500 isshown in FIG. 27 and may include a plurality of sensors 8506communicatively coupled to a controller 8502. The controller 8502 mayinclude a data acquisition circuit 8504, a signal evaluation circuit8508 and a response circuit 8510. The plurality of sensors 8506 may bewired to ports on the data acquisition circuit 8504 or wirelessly incommunication with the data acquisition circuit 8504. The plurality ofsensors 8506 may be wirelessly connected to the data acquisition circuit8504. The data acquisition circuit 8504 may be able to access detectionvalues corresponding to the output of at least one of the plurality ofsensors 8506 where the sensors 8506 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 8506 for a data monitoringdevice 8500 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, reliability of thesensors, and the like. The impact of failure may drive the extent towhich a component or piece of equipment is monitored with more sensorsand/or higher capability sensors being dedicated to systems whereunexpected or undetected failure would be costly or have severeconsequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8506 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor, a current sensor,an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an acoustic wave sensor, adisplacement sensor, a turbidity meter, a viscosity meter, a loadsensor, a tri-axial sensor, an accelerometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an acoustical sensor, a pH sensor, andthe like, including, without limitation, any of the sensors describedthroughout this disclosure and the documents incorporated by reference.

The sensors 8506 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8506 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 8506 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 27, the sensors 8506 may be partof the data monitoring device 8500, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 28 and 29,sensors 8518, either new or previously attached to or integrated intothe equipment or component, may be opportunistically connected to oraccessed by a monitoring device 8512. The sensors 8518 may be directlyconnected to input ports 8520 on the data acquisition circuit 8516 of acontroller 8514 or may be accessed by the data acquisition circuit 8516wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments, a data acquisition circuit 8516 may access detection valuescorresponding to the sensors 8518 wirelessly or via a separate source orsome combination of these methods. In embodiments, the data acquisitioncircuit 8504 may include a wireless communications circuit 8522 able towirelessly receive data opportunistically from sensors 8518 in thevicinity and route the data to the input ports 8520 on the dataacquisition circuit 8516.

In an embodiment, as illustrated in FIGS. 30 and 31, the signalevaluation circuit 8508 may then process the detection values to obtaininformation about the component or piece of equipment being monitored.Information extracted by the signal evaluation circuit 8508 may compriserotational speed, vibrational data including amplitudes, frequencies,phase, and/or acoustical data, and/or non-phase sensor data such astemperature, humidity, image data, and the like.

The signal evaluation circuit 8508 may include one or more componentssuch as a phase detection circuit 8528 to determine a phase differencebetween two time-based signals, a phase lock loop circuit 8530 to adjustthe relative phase of a signal such that it is aligned with a secondsignal, timer or reference signal, and/or a band pass filter circuit8532 which may be used to separate out signals occurring at differentfrequencies. An example band pass filter circuit 8532 includes anyfiltering operations understood in the art, including at least alow-pass filter, a high-pass filter, and/or a band pass filter—forexample to exclude or reduce frequencies that are not of interest for aparticular determination, and/or to enhance the signal for frequenciesof interest. Additionally, or alternatively, a band pass filter circuit8532 includes one or more notch filters or other filtering mechanism tonarrow ranges of frequencies (e.g., frequencies from a known source ofnoise). This may be used to filter out dominant frequency signals suchas the overall rotation, and may help enable the evaluation of lowamplitude signals at frequencies associated with torsion, bearingfailure and the like.

In embodiments, understanding the relative differences may be enabled bya phase detection circuit 8528 to determine a phase difference betweentwo signals. It may be of value to understand a relative phase offset,if any, between signals such as when a periodic vibration occursrelative to a relative rotation of a piece of equipment. In embodiments,there may be value in understanding where in a cycle shaft vibrationsoccur relative to a motor control input to better balance the control ofthe motor. This may be particularly true for systems and components thatare operating at relative slow RPMs. Understanding of the phasedifference between two signals or between those signals and a timer mayenable establishing a relationship between a signal value and where itoccurs in a process or rotation. Understanding relative phasedifferences may help in evaluating the relationship between differentcomponents of a system such as in the creation of a vibrational modelfor an Operational Deflection Shape (ODS).

The signal evaluation circuit 8544 may perform frequency analysis usingtechniques such as a digital Fast Fourier transform (FFT), Laplacetransform, Z-transform, wavelet transform, other frequency domaintransform, or other digital or analog signal analysis techniques,including, without limitation, complex analysis, including complex phaseevolution analysis. An overall rotational speed or tachometer may bederived from data from sensors such as rotational velocity meters,accelerometers, displacement meters and the like. Additional frequenciesof interest may also be identified. These may include frequencies nearthe overall rotational speed as well as frequencies higher than that ofthe rotational speed. These may include frequencies that arenonsynchronous with an overall rotational speed. Signals observed atfrequencies that are multiples of the rotational speed may be due tobearing induced vibrations or other behaviors or situations involvingbearings. In some instances, these frequencies may be in the range ofone times the rotational speed, two times the rotational speed, threetimes the rotational speed, and the like, up to 3.15 to 15 times therotational speed, or higher. In some embodiments, the signal evaluationcircuit 8544 may select RC components for a band pass filter circuit8532 based on overall rotational speed to create a band pass filtercircuit 8532 to remove signals at expected frequencies such as theoverall rotational speed, to facilitate identification of smallamplitude signals at other frequencies. In embodiments, variablecomponents may be selected, such that adjustments may be made in keepingwith changes in the rotational speed, so that the band pass filter maybe a variable band pass filter. This may occur under control ofautomatically self-adjusting circuit elements, or under control of aprocessor, including automated control based on a model of the circuitbehavior, where a rotational speed indicator or other data is providedas a basis for control.

In embodiments, rather than performing frequency analysis, the signalevaluation circuit 8544 may utilize the time-based detection values toperform transitory signal analysis. These may include identifying abruptchanges in signal amplitude including changes where the change inamplitude exceeds a predetermined value or exists for a certainduration. In embodiments, the time-based sensor data may be aligned witha timer or reference signal allowing the time-based sensor data to bealigned with, for example, a time or location in a cycle. Additionalprocessing to look at frequency changes over time may include the use ofShort-Time Fourier Transforms (STFT) or a wavelet transform.

In embodiments, frequency-based techniques and time-based techniques maybe combined, such as using time-based techniques to determine discretetime periods during which given operational modes or states areoccurring and using frequency-based techniques to determine behaviorwithin one or more of the discrete time periods.

In embodiments, the signal evaluation circuit may utilize demodulationtechniques for signals obtained from equipment running at slow speedssuch as paper and pulp machines, mining equipment, and the like. Asignal evaluation circuit employing a demodulation technique maycomprise a band-pass filter circuit, a rectifier circuit, and/or a lowpass circuit prior to transforming the data to the frequency domain.

The response circuit 8510 8710 may further comprise evaluating theresults of the signal evaluation circuit 8508 8544 and, based on certaincriteria, initiating an action. Criteria may include a predeterminedmaximum or minimum value for a detection value from a specific sensor, avalue of a sensor's corresponding detection value over time, a change invalue, a rate of change in value, and/or an accumulated value (e.g., atime spent above/below a threshold value, a weighted time spentabove/below one or more threshold values, and/or an area of the detectedvalue above/below one or more threshold values). The criteria mayinclude a sensor's detection values at certain frequencies or phaseswhere the frequencies or phases may be based on the equipment geometry,equipment control schemes, system input, historical data, currentoperating conditions, and/or an anticipated response. The criteria maycomprise combinations of data from different sensors such as relativevalues, relative changes in value, relative rates of change in value,relative values over time, and the like. The relative criteria maychange with other data or information such as process stage, type ofproduct being processed, type of equipment, ambient temperature andhumidity, external vibrations from other equipment, and the like. Therelative criteria may include level of synchronicity with an overallrotational speed, such as to differentiate between vibration induced bybearings and vibrations resulting from the equipment design. Inembodiments, the criteria may be reflected in one or more calculatedstatistics or metrics (including ones generated by further calculationson multiple criteria or statistics), which in turn may be used forprocessing (such as on board a data collector or by an external system),such as to be provided as an input to one or more of the machinelearning capabilities described in this disclosure, to a control system(which may be an on-board data collector or remote, such as to controlselection of data inputs, multiplexing of sensor data, storage, or thelike), or as a data element that is an input to another system, such asa data stream or data package that may be available to a datamarketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

In an illustrative and non-limiting example, an alert may be issued ifthe vibrational amplitude and/or frequency exceeds a predeterminedmaximum value, if there is a change or rate of change that exceeds apredetermined acceptable range, and/or if an accumulated value based onvibrational amplitude and/or frequency exceeds a threshold. Certainembodiments are described herein as detected values exceeding thresholdsor predetermined values, but detected values may also fall belowthresholds or predetermined values—for example where an amount of changein the detected value is expected to occur, but detected values indicatethat the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure. Based on vibration phaseinformation, a physical location of a problem may be identified. Basedon the vibration phase information system design flaws, off-nominaloperation, and/or component or process failures may be identified. Insome embodiments, an alert may be issued based on changes or rates ofchange in the data over time such as increasing amplitude or shifts inthe frequencies or phases at which a vibration occurs. In someembodiments, an alert may be issued based on accumulated values such astime spent over a threshold, weighted time spent over one or morethresholds, and/or an area of a curve of the detected value over one ormore thresholds. In embodiments, an alert may be issued based on acombination of data from different sensors such as relative changes invalue, or relative rates of change in amplitude, frequency of phase inaddition to values of non-phase sensors such as temperature, humidityand the like. For example, an increase in temperature and energy atcertain frequencies may indicate a hot bearing that is starting to fail.In embodiments, the relative criteria for an alarm may change with otherdata or information such as process stage, type of product beingprocessed on equipment, ambient temperature and humidity, externalvibrations from other equipment and the like.

In embodiments, response circuit 8510 may cause the data acquisitioncircuit 8504 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. Switching may be undertaken based ona model, a set of rules, or the like. In embodiments, switching may beunder control of a machine learning system, such that switching iscontrolled based on one or more metrics of success, combined with inputdata, over a set of trials, which may occur under supervision of a humansupervisor or under control of an automated system. Switching mayinvolve switching from one input port to another (such as to switch fromone sensor to another). Switching may involve altering the multiplexingof data, such as combining different streams under differentcircumstances. Switching may involve activating a system to obtainadditional data, such as moving a mobile system (such as a robotic ordrone system), to a location where different or additional data isavailable (such as positioning an image sensor for a different view orpositioning a sonar sensor for a different direction of collection) orto a location where different sensors can be accessed (such as moving acollector to connect up to a sensor that is disposed at a location in anenvironment by a wired or wireless connection). The response circuit8510 may make recommendations for the replacement of certain sensors inthe future with sensors having different response rates, sensitivity,ranges, and the like. The response circuit 8510 may recommend designalterations for future embodiments of the component, the piece ofequipment, the operating conditions, the process, and the like.

In embodiments, the response circuit 8510 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8510 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8510 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments, as shown in FIG. 32, the data monitoring device 8540 mayfurther comprise a data storage circuit 8542, memory, and the like. Thesignal evaluation circuit 8544 may periodically store certain detectionvalues to enable the tracking of component performance over time.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8544 may store data in the data storagecircuit 8542 based on the fit of data relative to one or more criteria,such as those described throughout this disclosure. Based on one sensorinput meeting or approaching specified criteria or range, the signalevaluation circuit 8544 may store additional data such as RPMs,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure. The signalevaluation circuit 8544 may store data at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

In embodiments, as shown in FIG. 33, a data monitoring system 8546 maycomprise at least one data monitoring device 8548. The at least one datamonitoring device 8548 comprising sensors 8506, a controller 8550comprising a data acquisition circuit 8504, a signal evaluation circuit8538, a data storage circuit 8542, and a communications circuit 8552 toallow data and analysis to be transmitted to a monitoring application8556 on a remote server 8554. The signal evaluation circuit 8538 maycomprise at least one of a phase detection circuit 8528, a phase lockloop circuit 8530, and/or a band pass circuit 8532. The signalevaluation circuit 8538 may periodically share data with thecommunication circuit 8552 for transmittal to the remote server 8554 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 8556. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the signal evaluation circuit 8538may share data with the communication circuit 8552 for transmittal tothe remote server 8554 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the signal evaluation circuit 8538 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The signal evaluation circuit8538 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server.

In embodiments, as illustrated in FIG. 34, a data collection system 8560may have a plurality of monitoring devices 8558 collecting data onmultiple components in a single piece of equipment, collecting data onthe same component across a plurality of pieces of equipment (both thesame and different types of equipment) in the same facility, as well ascollecting data from monitoring devices in multiple facilities. Amonitoring application on a remote server may receive and store the datacoming from a plurality of the various monitoring devices. Themonitoring application may then select subsets of data which may bejointly analyzed. Subsets of monitoring data may be selected based ondata from a single type of component or data from a single type ofequipment in which the component is operating. Monitoring data may beselected or grouped based on common operating conditions such as size ofload, operational condition (e.g., intermittent, continuous), operatingspeed or tachometer, common ambient environmental conditions such ashumidity, temperature, air or fluid particulate, and the like.Monitoring data may be selected based on the effects of other nearbyequipment, such as nearby machines rotating at similar frequencies,nearby equipment producing electromagnetic fields, nearby equipmentproducing heat, nearby equipment inducing movement or vibration, nearbyequipment emitting vapors, chemicals or particulates, or otherpotentially interfering or intervening effects.

The monitoring application may then analyze the selected data set. Forexample, data from a single component may be analyzed over differenttime periods such as one operating cycle, several operating cycles, amonth, a year, or the like. Data from multiple components of the sametype may also be analyzed over different time periods. Trends in thedata such as changes in frequency or amplitude may be correlated withfailure and maintenance records associated with the same component orpiece of equipment. Trends in the data such as changing rates of changeassociated with start-up or different points in the process may beidentified. Additional data may be introduced into the analysis such asoutput product quality, output quantity (such as per unit of time),indicated success or failure of a process, and the like. Correlation oftrends and values for different types of data may be analyzed toidentify those parameters whose short-term analysis might provide thebest prediction regarding expected performance. This information may betransmitted back to the monitoring device to update types of datacollected and analyzed locally or to influence the design of futuremonitoring devices.

In an illustrative and non-limiting example, the monitoring device maybe used to collect and process sensor data to measure mechanical torque.The monitoring device may be in communication with or include a highresolution, high speed vibration sensor to collect data over an extendedperiod of time, enough to measure multiple cycles of rotation. For geardriven equipment, the sampling resolution should be such that the numberof samples taken per cycle is at least equal to the number of gear teethdriving the component. It will be understood that a lower samplingresolution may also be utilized, which may result in a lower confidencedetermination and/or taking data over a longer period of time to developsufficient statistical confidence. This data may then be used in thegeneration of a phase reference (relative probe) or tachometer signalfor a piece of equipment. This phase reference may be used to alignphase data such as vibrational data or acceleration data from multiplesensors located at different positions on a component or on differentcomponents within a system. This information may facilitate thedetermination of torque for different components or the generation of anOperational Deflection Shape (ODS), indicating the extent of mechanicaldeflection of one or more components during an operational mode, whichin turn may be used to measure mechanical torque in the component.

The higher resolution data stream may provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component.

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up through ramping up to operating speed and thenduring operation. Once at operating speed, it is anticipated that thetorsional jitter should be minimal and changes in torsion during thisphase may be indicative of cracks, bearing faults and the like.Additionally, known torsions may be removed from the signal tofacilitate in the identification of unanticipated torsions resultingfrom system design flaws or component wear. Having phase informationassociated with the data collected at operating speed may facilitateidentification of a location of vibration and potential component wear.Relative phase information for a plurality of sensors located throughouta machine may facilitate the evaluation of torsion as it is propagatedthrough a piece of equipment.

An example system data collection in an industrial environment includesa data acquisition circuit that interprets a number of detection valuesfrom a number of input sensors communicatively coupled to the dataacquisition circuit, each of the number of detection valuescorresponding to at least one of the input sensors, a signal evaluationcircuit that obtains at least one of a vibration amplitude, a vibrationfrequency and a vibration phase location corresponding to at least oneof the input sensors in response to the number of detection values, anda response circuit that performs at least one operation in response toat the at least one of the vibration amplitude, the vibration frequencyand the vibration phase location. Certain further embodiments of anexample system include: where the signal evaluation circuit includes aphase detection circuit, or a phase detection circuit and a phase lockloop circuit and/or a band pass filter; where the number of inputsensors includes at least two input sensors providing phase informationand at least one input sensor providing non-phase sensor information;the signal evaluation circuit further aligning the phase informationprovided by the at least two of the input sensors; where the at leastone operation is further in response to at least one of: a change inmagnitude of the vibration amplitude; a change in frequency or phase ofvibration; a rate of change in at least one of vibration amplitude,vibration frequency and vibration phase; a relative change in valuebetween at least two of vibration amplitude, vibration frequency andvibration phase; and/or a relative rate of change between at least twoof vibration amplitude, vibration frequency, and vibration phase; thesystem further including an alert circuit, where the at least oneoperation includes providing an alert and where the alert may be one ofhaptic, audible and visual; a data storage circuit, where at least oneof the vibration amplitude, vibration frequency, and vibration phase isstored periodically to create a vibration history, and where the atleast one operation includes storing additional data in the data storagecircuit (e.g., as a vibration fingerprint for a component); where thestoring additional data in the data storage circuit is further inresponse to at least one of: a change in magnitude of the vibrationamplitude; a change in frequency or phase of vibration; a rate of changein the vibration amplitude, frequency or phase; a relative change invalue between at least two of vibration amplitude, frequency and phase;and a relative rate of change between at least two of vibrationamplitude, frequency and phase; the system further comprising at leastone of a multiplexing (MUX) circuit whereby alternative combinations ofdetection values may be selected based on at least one of user input, adetected state, and a selected operating parameter for a machine; whereeach of the number of detection values corresponds to at least one ofthe input sensors; where the at least one operation includes enabling ordisabling the connection of one or more portions of the multiplexingcircuit; a MUX control circuit that interprets a subset of the number ofdetection values and provides the logical control of the MUX and thecorrespondence of MUX input and detected values as a result; and/orwhere the logic control of the MUX includes adaptive scheduling of theselect lines.

An example method of monitoring a component, includes receivingtime-based data from at least one sensor, phase-locking the receiveddata with a reference signal, transforming the received time-based datato frequency data, filtering the frequency data to remove tachometerfrequencies, identifying low amplitude signals occurring at highfrequencies, and activating an alarm if a low amplitude signal exceeds athreshold.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes a plurality of monitoringdevices, each monitoring device comprising a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a signal evaluation circuit structuredto obtain at least one of vibration amplitude, vibration frequency and avibration phase location corresponding to at least one of the inputsensors in response to the corresponding at least one of the pluralityof detection values; a data storage facility for storing a subset of theplurality of detection values; a communication circuit structured tocommunicate at least one selected detection value to a remote server;and a monitoring application on the remote server structured to: receivethe at least one selected detection value; jointly analyze a subset ofthe detection values received from the plurality of monitoring devices;and recommend an action.

In certain further embodiments, an example system includes: for eachmonitoring device, the plurality of input sensors include at least oneinput sensor providing phase information and at least one input sensorproviding non-phase input sensor information and where joint analysisincludes using the phase information from the plurality of monitoringdevices to align the information from the plurality of monitoringdevices; where the subset of detection values is selected based on dataassociated with a detection value including at least one: common type ofcomponent, common type of equipment, and common operating conditions andfurther selected based on one of anticipated life of a componentassociated with detection values, type of the equipment associated withdetection values, and operational conditions under which detectionvalues were measured; and/or where the analysis of the subset ofdetection values includes feeding a neural net with the subset ofdetection values and supplemental information to learn to recognizevarious operating states, health states, life expectancies and faultstates utilizing deep learning techniques, wherein the supplementalinformation comprises one of component specification, componentperformance, equipment specification, equipment performance, maintenancerecords, repair records and an anticipated state model.

An example system for data collection in an industrial environmentincludes a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; asignal evaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to at least one of the input sensors in response to thecorresponding at least one of a plurality of detection values; amultiplexing circuit whereby alternative combinations of the detectionvalues may be selected based on at least one of user input, a detectedstate and a selected operating parameter for a machine, each of theplurality of detection values corresponding to at least one of the inputsensors; and a response circuit structured to perform at least oneoperation in response to at the at least one of the vibration amplitude,vibration frequency and vibration phase location.

An example system for data collection in a piece of equipment, includesa data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; atimer circuit structured to generate a timing signal based on a firstdetected value of the plurality of detection values; a signal evaluationcircuit structured to obtain at least one of vibration amplitude,vibration frequency and vibration phase location corresponding to asecond detected value comprising: a phase detection circuit structuredto determine a relative phase difference between a second detectionvalue of the plurality of detection values and the timing signal; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of the vibration amplitude, vibrationfrequency and vibration phase location.

An example system for bearing analysis in an industrial environment,includes a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage for storing specifications and anticipated stateinformation for a plurality of bearing types and buffering the pluralityof detection values for a predetermined length of time; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a bearing analysis circuitstructured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a lifeprediction comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value: and a response circuitstructured to perform at least one operation in response to at the atleast one of the vibration amplitude, vibration frequency and vibrationphase location.

An example motor monitoring system includes: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor the motor and motor components, store historical motor performanceand buffer the plurality of detection values for a predetermined lengthof time; a timer circuit structured to generate a timing signal based ona first detected value of the plurality of detection values; a motoranalysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a motor performance parameter comprising: a phase detection circuitstructured to determine a relative phase difference between a seconddetection value of the plurality of detection values and the timingsignal; and a signal evaluation circuit structured to obtain at leastone of vibration amplitude, vibration frequency and vibration phaselocation corresponding to a second detected value and analyze the atleast one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in a motor performanceparameter; and a response circuit structured to perform at least oneoperation in response to at the at least one of vibration amplitude,vibration frequency and vibration phase location and motor performanceparameter.

An example system for estimating a vehicle steering system performanceparameter, includes: a data acquisition circuit structured to interpreta plurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for the vehiclesteering system, the rack, the pinion, and the steering column, storehistorical steering system performance and buffer the plurality ofdetection values for a predetermined length of time;

a timer circuit structured to generate a timing signal based on a firstdetected value of the plurality of detection values; a steering systemanalysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a steering system performance parameter comprising: a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; and a signal evaluation circuit structured to obtain atleast one of vibration amplitude, vibration frequency and vibrationphase location corresponding to a second detected value and analyze theat least one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in a steering system performanceparameter; and a response circuit structured to perform at least oneoperation in response to at the at least one of vibration amplitude,vibration frequency and vibration phase location and the steering systemperformance parameter.

An example system for estimating a health parameter a pump performanceparameter includes a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for the pump and pumpcomponents associated with the detection values, store historical pumpperformance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a pump analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a pump performance parameter comprising: aphase detection circuit structured to determine a relative phasedifference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a pump performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the pump performance parameter, wherein the pump isone of a water pump in a car and a mineral pump.

An example system for estimating a drill performance parameter for adrilling machine, includes: a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe drill and drill components associated with the detection values,store historical drill performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a drill analysis circuit structured toanalyze buffered detection values relative to specifications andanticipated state information resulting in a drill performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a drill performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the drill performance parameter, wherein the drillingmachine is one of an oil drilling machine and a gas drilling machine.

An example system for estimating a conveyor health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a conveyor and conveyorcomponents associated with the detection values, store historicalconveyor performance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a conveyor analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in a conveyor performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in a conveyor performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the conveyor performance parameter.

An example system for estimating an agitator health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for an agitator and agitatorcomponents associated with the detection values, store historicalagitator performance and buffer the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; an agitator analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in an agitator performance parametercomprising: a phase detection circuit structured to determine a relativephase difference between a second detection value of the plurality ofdetection values and the timing signal; and a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value and analyze the at least one of vibration amplitude,vibration frequency and vibration phase location relative to buffereddetection values, specifications and anticipated state informationresulting in an agitator performance parameter; and a response circuitstructured to perform at least one operation in response to at the atleast one of vibration amplitude, vibration frequency and vibrationphase location and the agitator performance parameter, wherein theagitator is one of a rotating tank mixer, a large tank mixer, a portabletank mixers, a tote tank mixer, a drum mixer, a mounted mixer and apropeller mixer.

An example system for estimating a compressor health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a compressor andcompressor components associated with the detection values, storehistorical compressor performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a compressor analysis circuit structuredto analyze buffered detection values relative to specifications andanticipated state information resulting in a compressor performanceparameter comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value and analyze the at least one ofvibration amplitude, vibration frequency and vibration phase locationrelative to buffered detection values, specifications and anticipatedstate information resulting in a compressor performance parameter; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of vibration amplitude, vibrationfrequency and vibration phase location and the compressor performanceparameter.

An example system for estimating an air conditioner health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for an air conditioner andair conditioner components associated with the detection values, storehistorical air conditioner performance and buffer the plurality ofdetection values for a predetermined length of time; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; an air conditioner analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in an airconditioner performance parameter comprising: a phase detection circuitstructured to determine a relative phase difference between a seconddetection value of the plurality of detection values and the timingsignal; and a signal evaluation circuit structured to obtain at leastone of vibration amplitude, vibration frequency and vibration phaselocation corresponding to a second detected value and analyze the atleast one of vibration amplitude, vibration frequency and vibrationphase location relative to buffered detection values, specifications andanticipated state information resulting in an air conditionerperformance parameter; and a response circuit structured to perform atleast one operation in response to at the at least one of vibrationamplitude, vibration frequency and vibration phase location and the airconditioner performance parameter.

An example system for estimating a centrifuge health parameter,includes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a centrifuge andcentrifuge components associated with the detection values, storehistorical centrifuge performance and buffer the plurality of detectionvalues for a predetermined length of time; a timer circuit structured togenerate a timing signal based on a first detected value of theplurality of detection values; a centrifuge analysis circuit structuredto analyze buffered detection values relative to specifications andanticipated state information resulting in a centrifuge performanceparameter comprising: a phase detection circuit structured to determinea relative phase difference between a second detection value of theplurality of detection values and the timing signal; and a signalevaluation circuit structured to obtain at least one of vibrationamplitude, vibration frequency and vibration phase locationcorresponding to a second detected value and analyze the at least one ofvibration amplitude, vibration frequency and vibration phase locationrelative to buffered detection values, specifications and anticipatedstate information resulting in a centrifuge performance parameter; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of vibration amplitude, vibrationfrequency and vibration phase location and the centrifuge performanceparameter.

In embodiments, information about the health of a component or piece ofindustrial equipment may be obtained by comparing the values of multiplesignals at the same point in a process. This may be accomplished byaligning a signal relative to other related data signals, timers, orreference signals. An embodiment of a data monitoring device 8700, 8718is shown in FIGS. 35-37 and may include a controller 8702, 8720. Thecontroller may include a data acquisition circuit 8704, 8722, a signalevaluation circuit 8708, a data storage circuit 8716 and an optionalresponse circuit 8710. The signal evaluation circuit 8708 may comprise atimer circuit 8714 and, optionally, a phase detection circuit 8712.

The data monitoring device may include a plurality of sensors 8706communicatively coupled to a controller 8702. The plurality of sensors8706 may be wired to ports on the data acquisition circuit 8704. Theplurality of sensors 8706 may be wirelessly connected to the dataacquisition circuit 8704 which may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors8706 where the sensors 8706 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.In embodiments, as illustrated in FIGS. 36 and 37, one or more externalsensors 8724 which are not explicitly part of a monitoring device 8718may be opportunistically connected to or accessed by the monitoringdevice 8718. The data acquisition circuit 8722 may include one or moreinput ports 8726. The one or more external sensors 8724 may be directlyconnected to the one or more input ports 8726 on the data acquisitioncircuit 8722 of the controller 8720. In embodiments, as shown in FIG.37, a data acquisition circuit 8722 may further comprise a wirelesscommunications circuit 8728 to access detection values corresponding tothe one or more external sensors 8724 wirelessly or via a separatesource or some combination of these methods.

The selection of the plurality of sensors 8706, 8724 for connection to adata monitoring device 8700 8718 designed for a specific component orpiece of equipment may depend on a variety of considerations such asaccessibility for installing new sensors, incorporation of sensors inthe initial design, anticipated operational and failure conditions,resolution desired at various positions in a process or plant,reliability of the sensors, and the like. The impact of a failure, timeresponse of a failure (e.g., warning time and/or off-nominal modesoccurring before failure), likelihood of failure, and/or sensitivityrequired and/or difficulty to detect failed conditions may drive theextent to which a component or piece of equipment is monitored with moresensors and/or higher capability sensors being dedicated to systemswhere unexpected or undetected failure would be costly or have severeconsequences.

The signal evaluation circuit 8708 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 8708may comprise information regarding what point or time in a processcorresponds with a detection value where the point in time is based on atiming signal generated by the timer circuit 8714. The start of thetiming signal may be generated by detecting an edge of a control signalsuch as a rising edge, falling edge or both where the control signal maybe associated with the start of a process. The start of the timingsignal may be triggered by an initial movement of a component or pieceof equipment. The start of the timing signal may be triggered by aninitial flow through a pipe or opening or by a flow achieving apredetermined rate. The start of the timing signal may be triggered by astate value indicating a process has commenced—for example the state ofa switch, button, data value provided to indicate the process hascommenced, or the like. Information extracted may comprise informationregarding a difference in phase, determined by the phase detectioncircuit 8712, between a stream of detection value and the time signalgenerated by the timer circuit 8714. Information extracted may compriseinformation regarding a difference in phase between one stream ofdetection values and a second stream of detection values where the firststream of detection values is used as a basis or trigger for a timingsignal generated by the timer circuit.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors8706, 8724 may comprise one or more of, without limitation, athermometer, a hygrometer, a voltage sensor, a current sensor, anaccelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a displacementsensor, a turbidity meter, a viscosity meter, a load sensor, a tri-axialsensor, a tachometer, a fluid pressure meter, an air flow meter, ahorsepower meter, a flow rate meter, a fluid particle detector, anacoustical sensor, a pH sensor, and the like.

The sensors 8706, 8724 may provide a stream of data over time that has aphase component, such as acceleration or vibration, allowing for theevaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors8706, 8724 may provide a stream of data that is not phase based such astemperature, humidity, load, and the like. The sensors 8706, 8724 mayprovide a continuous or near continuous stream of data over time,periodic readings, event-driven readings, and/or readings according to aselected interval or schedule.

In embodiments, as illustrated in FIGS. 38 and 39, the data acquisitioncircuit 8734 may further comprise a multiplexer circuit 8736 asdescribed elsewhere herein. Outputs from the multiplexer circuit 8736may be utilized by the signal evaluation circuit 8708. The responsecircuit 8710 may have the ability to turn on and off portions of themultiplexer circuit 8736. The response circuit 8710 may have the abilityto control the control channels of the multiplexer circuit 8736.

The response circuit 8710 may further comprise evaluating the results ofthe signal evaluation circuit 8708 and, based on certain criteria,initiating an action. The criteria may include a sensor's detectionvalues at certain frequencies or phases relative to the timer signalwhere the frequencies or phases of interest may be based on theequipment geometry, equipment control schemes, system input, historicaldata, current operating conditions, and/or an anticipated response.Criteria may include a predetermined maximum or minimum value for adetection value from a specific sensor, a cumulative value of a sensor'scorresponding detection value over time, a change in value, a rate ofchange in value, and/or an accumulated value (e.g., a time spentabove/below a threshold value, a weighted time spent above/below one ormore threshold values, and/or an area of the detected value above/belowone or more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In some embodiments, an alert may be issued based on the some of thecriteria discussed above. In an illustrative example, an increase intemperature and energy at certain frequencies may indicate a hot bearingthat is starting to fail. In embodiments, the relative criteria for analarm may change with other data or information such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 8710 mayinitiate an alert if a vibrational amplitude and/or frequency exceeds apredetermined maximum value, if there is a change or rate of change thatexceeds a predetermined acceptable range, and/or if an accumulated valuebased on vibrational amplitude and/or frequency exceeds a threshold.

In embodiments, response circuit 8710 may cause the data acquisitioncircuit 8704 to enable or disable the processing of detection valuescorresponding to certain sensors based on the some of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, and the like. This switching may be implemented bychanging the control signals for a multiplexer circuit 8736 and/or byturning on or off certain input sections of the multiplexer circuit8736. The response circuit 8710 may make recommendations for thereplacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 8710 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 8710 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 8710 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 8710 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational. In an illustrative example, vibration phaseinformation, derived by the phase detection circuit 8712 relative to atimer signal from the timer circuit 8714, may be indicative of aphysical location of a problem. Based on the vibration phaseinformation, system design flaws, off-nominal operation, and/orcomponent or process failures may be identified.

In embodiments, based on relevant operating conditions and/or failuremodes which may occur in as sensor values approach one or more criteria,the signal evaluation circuit 8708 may store data in the data storagecircuit 8716 based on the fit of data relative to one or more criteria.Based on one sensor input meeting or approaching specified criteria orrange, the signal evaluation circuit 8708 may store additional data suchas RPMs, component loads, temperatures, pressures, vibrations in thedata storage circuit 8716. The signal evaluation circuit 8708 may storedata at a higher data rate for greater granularity in future processing,the ability to reprocess at different sampling rates, and/or to enablediagnosing or post-processing of system information where operationaldata of interest is flagged, and the like.

In embodiments, as shown in FIGS. 40 and 41 and 42 and 43, a datamonitoring system 8762 may include at least one data monitoring device8768. The at least one data monitoring device 8768 may include sensors8706 and a controller 8770 comprising a data acquisition circuit 8704, asignal evaluation circuit 8772, a data storage circuit 8742, and acommunications circuit 8752 to allow data and analysis to be transmittedto a monitoring application 8776 on a remote server 8774. The signalevaluation circuit 8772 may include at least one of a phase detectioncircuit 8712 and a timer circuit 8714. The signal evaluation circuit8772 may periodically share data with the communication circuit 8752 fortransmittal to the remote server 8774 to enable the tracking ofcomponent and equipment performance over time and under varyingconditions by a monitoring application 8776. Because relevant operatingconditions and/or failure modes may occur as sensor values approach oneor more criteria, the signal evaluation circuit 8708 may share data withthe communication circuit 8752 for transmittal to the remote server 8774based on the fit of data relative to one or more criteria. Based on onesensor input meeting or approaching specified criteria or range, thesignal evaluation circuit 8708 may share additional data such as RPMs,component loads, temperatures, pressures, vibrations, and the like fortransmittal. The signal evaluation circuit 8772 may share data at ahigher data rate for transmittal to enable greater granularity inprocessing on the remote server.

In embodiments, as shown in FIG. 40, the communications circuit 8752 maycommunicated data directly to a remote server 8774. In embodiments, asshown in FIG. 41, the communications circuit 8752 may communicate datato an intermediate computer 8754 which may include a processor 8756running an operating system 8758 and a data storage circuit 8760. Theintermediate computer 8754 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8774.

In embodiments as illustrated in FIGS. 42 and 43, a data monitoringsystem 8762 may have a plurality of monitoring devices 8768 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.In embodiments, as shown in FIG. 42 the communications circuit 8752 maycommunicated data directly to a remote server 8774. In embodiments, asshown in FIG. 43, the communications circuit 8752 may communicate datato an intermediate computer 8754 which may include a processor 8756running an operating system 8758 and a data storage circuit 8760. Theintermediate computer 8754 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server8774.

In embodiments, a monitoring application 8776 on a remote server 8774may receive and store one or more of detection values, timing signalsand data coming from a plurality of the various monitoring devices 8768.The monitoring application 8776 may then select subsets of the detectionvalues, timing signals and data to be jointly analyzed. Subsets foranalysis may be selected based on a single type of component or a singletype of equipment in which a component is operating. Subsets foranalysis may be selected or grouped based on common operating conditionssuch as size of load, operational condition (e.g., intermittent,continuous, process stage), operating speed or tachometer, commonambient environmental conditions such as humidity, temperature, air orfluid particulate, and the like. Subsets for analysis may be selectedbased on the effects of other nearby equipment such as nearby machinesrotating at similar frequencies.

The monitoring application 8776 may then analyze the selected subset. Inan illustrative example, data from a single component may be analyzedover different time periods such as one operating cycle, severaloperating cycles, a month, a year, the life of the component or thelike. Data from multiple components of the same type may also beanalyzed over different time periods. Trends in the data such as changesin frequency or amplitude may be correlated with failure and maintenancerecords associated with the same or a related component or piece ofequipment. Trends in the data such as changing rates of changeassociated with start-up or different points in the process may beidentified. Additional data may be introduced into the analysis such asoutput product quality, indicated success or failure of a process, andthe like. Correlation of trends and values for different types of datamay be analyzed to identify those parameters whose short-term analysismight provide the best prediction regarding expected performance. Thisinformation may be transmitted back to the monitoring device to updatetypes of data collected and analyzed locally or to influence the designof future monitoring devices.

In an illustrative and non-limiting example, a monitoring device 8768may be used to collect and process sensor data to measure mechanicaltorque. The monitoring device 8768 may be in communication with orinclude a high resolution, high speed vibration sensor to collect dataover a period of time sufficient to measure multiple cycles of rotation.For gear driven components, the sampling resolution of the sensor shouldbe such that the number of samples taken per cycle is at least equal tothe number of gear teeth driving the component. It will be understoodthat a lower sampling resolution may also be utilized, which may resultin a lower confidence determination and/or taking data over a longerperiod of time to develop sufficient statistical confidence. This datamay then be used in the generation of a phase reference (relative probe)or tachometer signal for a piece of equipment. This phase reference maybe used directly or used by the timer circuit 8714 to generate a timingsignal to align phase data such as vibrational data or acceleration datafrom multiple sensors located at different positions on a component oron different components within a system. This information may facilitatethe determination of torque for different components or the generationof an Operational Deflection Shape (ODS).

A higher resolution data stream may also provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component operating a low RPMs.

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up, through ramping up to operating speed, and thenduring operation. Once at operating speed, it is anticipated that thetorsional jitter should be minimal or within expected ranges, andchanges in torsion during this phase may be indicative of cracks,bearing faults, and the like. Additionally, known torsions may beremoved from the signal to facilitate in the identification ofunanticipated torsions resulting from system design flaws, componentwear, or unexpected process events. Having phase information associatedwith the data collected at operating speed may facilitate identificationof a location of vibration and potential component wear, and/or may befurther correlated to a type of failure for a component. Relative phaseinformation for a plurality of sensors located throughout a machine mayfacilitate the evaluation of torsion as it is propagated through a pieceof equipment.

In embodiments, the monitoring application 8776 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for plurality ofcomponent types, operational history, historical detection values,component life models, and the like for use in analyzing the selectedsubset using rule-based or model-based analysis. In embodiments, themonitoring application 8776 may feed a neural net with the selectedsubset to learn to recognize various operating state, health states(e.g., lifetime predictions) and fault states utilizing deep learningtechniques. In embodiments, a hybrid of the two techniques (model-basedlearning and deep learning) may be used.

In an illustrative and non-limiting example, component health of:conveyors and lifters in an assembly line; water pumps on industrialvehicles; factory air conditioning units; drilling machines, screwdrivers, compressors, pumps, gearboxes, vibrating conveyors, mixers andmotors situated in the oil and gas fields; factory mineral pumps;centrifuges, and refining tanks situated in oil and gas refineries; andcompressors in gas handling systems may be monitored using the phasedetection and alignment techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the component health ofequipment to promote chemical reactions deployed in chemical andpharmaceutical production lines (e.g. rotating tank/mixer agitators,mechanical/rotating agitators, and propeller agitators) may be evaluatedusing the phase detection and alignment techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the component health ofvehicle steering mechanisms and/or vehicle engines may be evaluatedusing the phase detection and alignment techniques, data monitoringdevices and data collection systems described herein.

An example monitoring system for data collection, includes a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors communicatively coupled to thedata acquisition circuit; a signal evaluation circuit comprising: atimer circuit structured to generate at least one timing signal; and aphase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values andat least one of the timing signals from the timer circuit; and aresponse circuit structured to perform at least one operation inresponse to the relative phase difference. In certain furtherembodiments, an example system includes: wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one of the plurality of detection values; and a relativerate of change in amplitude and relative phase of at least one of theplurality of detection values; wherein the at least one operationcomprises issuing an alert; wherein the alert may be one of haptic,audible and visual; a data storage circuit, wherein the relative phasedifference and at least one of the detection values and the timingsignal are stored; wherein the at least one operation further comprisesstoring additional data in the data storage circuit; wherein the storingadditional data in the data storage circuit is further in response to atleast one of: a change in the relative phase difference and a relativerate of change in the relative phase difference; wherein the dataacquisition circuit further comprises at least one multiplexer circuit(MUX) whereby alternative combinations of detection values may beselected based on at least one of user input and a selected operatingparameter for a machine, wherein each of the plurality of detectionvalues corresponds to at least one of the input sensors; wherein the atleast one operation comprises enabling or disabling one or more portionsof the multiplexer circuit, or altering the multiplexer control lines;wherein the data acquisition circuit comprises at least two multiplexercircuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits; and/or the system furthercomprising a MUX control circuit structured to interpret a subset of theplurality of detection values and provide the logical control of the MUXand the correspondence of MUX input and detected values as a result,wherein the logic control of the MUX comprises adaptive scheduling ofthe select lines.

An example system for data collection, includes: a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; and a phase response circuit structured to perform atleast one operation in response to the phase difference. In certainfurther embodiments, an example system includes wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one of the plurality of detection values and a relative rateof change in amplitude and relative phase of at least one of theplurality of detection values; wherein the at least one operationcomprises issuing an alert; wherein the alert may be one of haptic,audible and visual; where the system, further includes a data storagecircuit; wherein the relative phase difference and at least one of thedetection values and the timing signal are stored; wherein the at leastone operation further includes storing additional data in the datastorage circuit; wherein the storing additional data in the data storagecircuit is further in response to at least one of: a change in therelative phase difference and a relative rate of change in the relativephase difference; wherein the data acquisition circuit further includesat least one multiplexer (MUX) circuit whereby alternative combinationsof detection values may be selected based on at least one of user inputand a selected operating parameter for a machine; wherein each of theplurality of detection values corresponds to at least one of the inputsensors; wherein the at least one operation comprises enabling ordisabling one or more portions of the multiplexer circuit, or alteringthe multiplexer control lines; wherein the data acquisition circuitcomprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits; where the system further comprising a MUX controlcircuit structured to interpret a subset of the plurality of detectionvalues and provide the logical control of the MUX and the correspondenceof MUX input and detected values as a result; and/or wherein the logiccontrol of the MUX comprises adaptive scheduling of the select lines.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a signal evaluation circuit comprising: a timercircuit structured to generate a timing signal based on a first detectedvalue of the plurality of detection values; and a phase detectioncircuit structured to determine a relative phase difference between asecond detection value of the plurality of detection values and thetiming signal; a data storage facility for storing a subset of theplurality of detection values and the timing signal; a communicationcircuit structured to communicate at least one selected detection valueand the timing signal to a remote server; and a monitoring applicationon the remote server structured to receive the at least one selecteddetection value and the timing signal; jointly analyze a subset of thedetection values received from the plurality of monitoring devices; andrecommend an action. In certain embodiments, the example system furtherincludes wherein joint analysis comprises using the timing signal fromeach of the plurality of monitoring devices to align the detectionvalues from the plurality of monitoring devices and/or wherein thesubset of detection values is selected based on data associated with adetection value comprising at least one: common type of component,common type of equipment, and common operating conditions.

An example system for data collection in an industrial environment,includes: a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit, the dataacquisition circuit comprising a multiplexer circuit whereby alternativecombinations of the detection values may be selected based on at leastone of user input, a detected state and a selected operating parameterfor a machine, each of the plurality of detection values correspondingto at least one of the input sensors; a signal evaluation circuitcomprising: a timer circuit structured to generate a timing signal; anda phase detection circuit structured to determine a relative phasedifference between at least one of the plurality of detection values anda signal from the timer circuit; and a response circuit structured toperform at least one operation in response to the phase difference.

An example monitoring system for data collection in a piece ofequipment, includes a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensorscommunicatively coupled to the data acquisition circuit; a timer circuitstructured to generate a timing signal based on a first detected valueof the plurality of detection values; a signal evaluation circuitstructured to obtain at least one of vibration amplitude, vibrationfrequency and vibration phase location corresponding to a seconddetected value comprising: a phase detection circuit structured todetermine a relative phase difference between a second detection valueof the plurality of detection values and the timing signal; and aresponse circuit structured to perform at least one operation inresponse to at the at least one of the vibration amplitude, vibrationfrequency and vibration phase location.

A monitoring system for bearing analysis in an industrial environment,the monitoring device includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a timercircuit structured to generate a timing signal a data storage forstoring specifications and anticipated state information for a pluralityof bearing types and buffering the plurality of detection values for apredetermined length of time; a timer circuit structured to generate atiming signal based on a first detected value of the plurality ofdetection values; a bearing analysis circuit structured to analyzebuffered detection values relative to specifications and anticipatedstate information resulting in a life prediction comprising: a phasedetection circuit structured to determine a relative phase differencebetween a second detection value of the plurality of detection valuesand the timing signal; a signal evaluation circuit structured to obtainat least one of vibration amplitude, vibration frequency and vibrationphase location corresponding to a second detected value: and a responsecircuit structured to perform at least one operation in response to atthe at least one of the vibration amplitude, vibration frequency andvibration phase location.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement and the like. An embodiment of a datamonitoring device 9000 is shown in FIG. 44 and may include a pluralityof sensors 9006 communicatively coupled to a controller 9002. Thecontroller 9002, which may be part of a data collection device, such asa mobile data collector, or part of a system, such as a network-deployedor cloud-deployed system, may include a data acquisition circuit 9004, asignal evaluation circuit 9008 and a response circuit 9010. The signalevaluation circuit 9008 may comprise a peak detection circuit 9012.Additionally, the signal evaluation circuit 9008 may optionally compriseone or more of a phase detection circuit 9016, a bandpass filter circuit9018, a phase lock loop circuit, a torsional analysis circuit, a bearinganalysis circuit, and the like. The bandpass filter 9018 may be used tofilter a stream of detection values such that values, such as peaks andvalleys, are detected only at or within bands of interest, such asfrequencies of interest. The data acquisition circuit 9004 may includeone or more analog-to-digital converter circuits 9014. A peak amplitudedetected by the peak detection circuit 9012 may be input into one ormore analog-to-digital converter circuits 9014 to provide a referencevalue for scaling output of the analog-to-digital converter circuits9014 appropriately.

The plurality of sensors 9006 may be wired to ports on the dataacquisition circuit 9004. The plurality of sensors 9006 may bewirelessly connected to the data acquisition circuit 9004. The dataacquisition circuit 9004 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9006 where the sensors 9006 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 9006 for a data monitoringdevice 9000 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors,power availability, power utilization, storage utilization, and thelike. The impact of a failure, time response of a failure (e.g., warningtime and/or off-optimal modes occurring before failure), likelihood offailure, extent of impact of failure, and/or sensitivity required and/ordifficulty to detection failure conditions may drive the extent to whicha component or piece of equipment is monitored with more sensors and/orhigher capability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

The signal evaluation circuit 9008 may process the detection values toobtain information about a component or piece of equipment beingmonitored. Information extracted by the signal evaluation circuit 9008may comprise information regarding a peak value of a signal such as apeak temperature, peak acceleration, peak velocity, peak pressure, peakweight bearing, peak strain, peak bending, or peak displacement. Thepeak detection may be done using analog or digital circuits. Inembodiments, the peak detection circuit 9012 may be able to distinguishbetween “local” or short term peaks in a stream of detection values anda “global” or longer term peak. In embodiments, the peak detectioncircuit 9012 may be able to identify peak shapes (not just a single peakvalue) such as flat tops, asymptotic approaches, discrete jumps in thepeak value or rapid/steep climbs in peak value, sinusoidal behaviorwithin ranges and the like. Flat topped peaks may indicate saturation atof a sensor. Asymptotic approaches to a peak may indicate linear systembehavior. Discrete jumps in value or steep changes in peak value mayindicate quantized or nonlinear behavior of either the sensor doing themeasurement or the behavior of the component. In embodiments, the systemmay be able to identify sinusoidal variations in the peak value withinan envelope, such as an envelope established by line or curve connectinga series of peak values. It should be noted that references to “peaks”should be understood to encompass one or more “valleys,” representing aseries of low points in measurement, except where context indicatesotherwise.

In embodiments, a peak value may be used as a reference for ananalog-to-digital converter circuit 9014.

In an illustrative and non-limiting example, a temperature probe maymeasure the temperature of a gear as it rotates in a machine. The peaktemperature may be detected by a peak detection circuit 9012. The peaktemperature may be fed into an analog-to-digital converter circuit 9014to appropriately scale a stream of detection values corresponding totemperature readings of the gear as it rotates in a machine. The phaseof the stream of detection values corresponding to temperature relativeto an orientation of the gear may be determined by the phase detectioncircuit 9016. Knowing where in the rotation of the gear a peaktemperature is occurring may allow the identification of a bad geartooth.

In some embodiments, two or more sets of detection values may be fusedto create detection values for a virtual sensor. A peak detectioncircuit may be used to verify consistency in timing of peak valuesbetween at least one of the two or more sets of detection values and thedetection values for the virtual sensor.

In embodiments, the signal evaluation circuit 9008 may be able to resetthe peak detection circuit 9012 upon start-up of the monitoring device9000, upon edge detection of a control signal of the system beingmonitored, based on a user input, after a system error and the like. Inembodiments, the signal evaluation circuit 9008 may discard an initialportion of the output of the peak detection circuit 9012 prior to usingthe peak value as a reference value for an analog-to-digital conversioncircuit to allow the system to fully come online.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors9006 may comprise one or more of, without limitation, a vibrationsensor, a thermometer, a hygrometer, a voltage sensor, a current sensor,an accelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an acoustic wave sensor, adisplacement sensor, a turbidity meter, a viscosity meter, a loadsensor, a tri-axial sensor, an accelerometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an acoustical sensor, a pH sensor, andthe like, including, without limitation, any of the sensors describedthroughout this disclosure and the documents incorporated by reference.

The sensors 9006 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9006 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9006 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 44, the sensors 9006 may be partof the data monitoring device 9000, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 45 and 46, oneor more external sensors 9026, which are not explicitly part of amonitoring device 9020 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9020. The monitoringdevice 9020 may include a controller 9022. The controller 9022 mayinclude a response circuit 9010, a signal evaluation circuit 9008 and adata acquisition circuit 9024. The signal evaluation circuit 9008 mayinclude a peak detection circuit 9012 and optionally a phase detectioncircuit 9016 and/or a bandpass filter circuit 9018. The data acquisitioncircuit 9024 may include one or more input ports 9028. The one or moreexternal sensors 9026 may be directly connected to the one or more inputports 9028 on the data acquisition circuit 9024 of the controller 9022or may be accessed by the data acquisition circuit 9004 wirelessly, suchas by a reader, interrogator, or other wireless connection, such as overa short-distance wireless protocol. In embodiments as shown in FIG. 46,a data acquisition circuit 9024 may further comprise a wirelesscommunication circuit 9030. The data acquisition circuit 9024 may usethe wireless communication circuit 9030 to access detection valuescorresponding to the one or more external sensors 9026 wirelessly or viaa separate source or some combination of these methods.

In embodiments as illustrated in FIG. 47, the data acquisition circuit9036 may further comprise a multiplexer circuit 9038 as describedelsewhere herein. Outputs from the multiplexer circuit 9038 may beutilized by the signal evaluation circuit 9008. The response circuit9010 may have the ability to turn on and off portions of the multiplexorcircuit 9038. The response circuit 9010 may have the ability to controlthe control channels of the multiplexor circuit 9038.

The response circuit 9010 may evaluate the results of the signalevaluation circuit 9008 and, based on certain criteria, initiate anaction. The criteria may include a predetermined peak value for adetection value from a specific sensor, a cumulative value of a sensor'scorresponding detection value over time, a change in peak value, a rateof change in a peak value, and/or an accumulated value (e.g., a timespent above/below a threshold value, a weighted time spent above/belowone or more threshold values, and/or an area of the detected valueabove/below one or more threshold values). The criteria may comprisecombinations of data from different sensors such as relative values,relative changes in value, relative rates of change in value, relativevalues over time, and the like. The relative criteria may change withother data or information such as process stage, type of product beingprocessed, type of equipment, ambient temperature and humidity, externalvibrations from other equipment, and the like. The relative criteria maybe reflected in one or more calculated statistics or metrics (includingones generated by further calculations on multiple criteria orstatistics), which in turn may be used for processing (such as anon-board a data collector or by an external system), such as to beprovided as an input to one or more of the machine learning capabilitiesdescribed in this disclosure, to a control system (which may be on-boarda data collector or remote, such as to control selection of data inputs,multiplexing of sensor data, storage, or the like), or as a data elementthat is an input to another system, such as a data stream or datapackage that may be available to a data marketplace, a SCADA system, aremote control system, a maintenance system, an analytic system, orother system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. For example, in aprocess involving a blender, a mixer, an agitator or the like, theabsence of vibration may indicate that a blade, fin, vane or otherworking element is unable to move adequately, such as, for example, as aresult of a working material being excessively viscous or as a result ofa problem in gears (e.g., stripped gears, seizing in gears, or the like(a clutch, or the like). Except where the context clearly indicatesotherwise, any description herein describing a determination of a valueabove a threshold and/or exceeding a predetermined or expected value isunderstood to include determination of a value below a threshold and/orfalling below a predetermined or expected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In embodiments, the response circuit 9010 may issue an alert based onone or more of the criteria discussed above. In an illustrative example,an increase in peak temperature beyond a predetermined value mayindicate a hot bearing that is starting to fail. In embodiments, therelative criteria for an alarm may change with other data or informationsuch as process stage, type of product being processed on equipment,ambient temperature and humidity, external vibrations from otherequipment and the like. In an illustrative and non-limiting example, theresponse circuit 9010 may initiate an alert if an amplitude, such as avibrational amplitude and/or frequency, exceeds a predetermined maximumvalue, if there is a change or rate of change that exceeds apredetermined acceptable range, and/or if an accumulated value based onsuch amplitude and/or frequency exceeds a threshold.

In embodiments, the response circuit 9010 may cause the data acquisitioncircuit 9004 to enable or disable the processing of detection valuescorresponding to certain sensors based on one or more of the criteriadiscussed above. This may include switching to sensors having differentresponse rates, sensitivity, ranges, and the like; accessing new sensorsor types of sensors, accessing data from multiple sensors, and the like.Switching may be based on a detected peak value for the sensor beingswitched or based on the peak value of another sensor. Switching may beundertaken based on a model, a set of rules, or the like. Inembodiments, switching may be under control of a machine learningsystem, such that switching is controlled based on one or more metricsof success, combined with input data, over a set of trials, which mayoccur under supervision of a human supervisor or under control of anautomated system. Switching may involve switching from one input port toanother (such as to switch from one sensor to another). Switching mayinvolve altering the multiplexing of data, such as combining differentstreams under different circumstances. Switching may involve activatinga system to obtain additional data, such as moving a mobile system (suchas a robotic or drone system), to a location where different oradditional data is available (such as positioning an image sensor for adifferent view or positioning a sonar sensor for a different directionof collection) or to a location where different sensors can be accessed(such as moving a collector to connect up to a sensor that is disposedat a location in an environment by a wired or wireless connection). Thisswitching may be implemented by changing the control signals for amultiplexor circuit 9038 and/or by turning on or off certain inputsections of the multiplexor circuit 9038.

In embodiments, the response circuit 9010 may adjust a sensor scalingvalue using the detected peak as a reference voltage. The responsecircuit 9010 may adjust a sensor sampling rate such that the peak valueis captured.

The response circuit 9010 may identify sensor overload. In embodiments,the response circuit 9010 may make recommendations for the replacementof certain sensors in the future with sensors having different responserates, sensitivity, ranges, and the like. The response circuit 9010 mayrecommend design alterations for future embodiments of the component,the piece of equipment, the operating conditions, the process, and thelike.

In embodiments, the response circuit 9010 may recommend maintenance atan upcoming process stop or initiate a maintenance call where themaintenance may include the replacement of the sensor with the same oran alternate type of sensor having a different response rate,sensitivity, range and the like. In embodiments, the response circuit9010 may implement or recommend process changes—for example, to lowerthe utilization of a component that is near a maintenance interval,operating off-nominally, or failed for purpose but still at leastpartially operational, to change the operating speed of a component(such as to put it in a lower-demand mode), to initiate amelioration ofan issue (such as to signal for additional lubrication of a rollerbearing set, or to signal for an alignment process for a system that isout of balance), and the like.

In embodiments, as shown in FIG. 48, the data monitoring device 9040 mayinclude sensors 9006 and a controller 9042 which may include a dataacquisition circuit 9004, and a signal evaluation circuit 9008. Thesignal evaluation circuit 9008 may include a peak detection circuit 9012and, optionally, a phased detection circuit 9016 and/or a bandpassfilter circuit 9018. The controller 9042 may further include a datastorage circuit 9044, memory, and the like. The controller 9042 mayfurther include a response circuit 9010. The signal evaluation circuit9008 may periodically store certain detection values in the data storagecircuit 9044 to enable the tracking of component performance over time.

In embodiments, based on relevant criteria as described elsewhereherein, operating conditions and/or failure modes which may occur assensor values approach one or more criteria, the signal evaluationcircuit 9008 may store data in the data storage circuit 9044 based onthe fit of data relative to one or more criteria, such as thosedescribed throughout this disclosure. Based on one sensor input meetingor approaching specified criteria or range, the signal evaluationcircuit 9008 may store additional data such as RPMs, component loads,temperatures, pressures, vibrations or other sensor data of the typesdescribed throughout this disclosure in the data storage circuit 9068.The signal evaluation circuit 9008 may store data at a higher data ratefor greater granularity in future processing, the ability to reprocessat different sampling rates, and/or to enable diagnosing orpost-processing of system information where operational data of interestis flagged, and the like.

In embodiments, the signal evaluation circuit 9008 may store new peaksthat indicate changes in overall scaling over a long duration (e.g.,scaling a data stream based on historical peaks over months ofanalysis). The signal evaluation circuit 9008 may store data whenhistorical peak values are approached (e.g., as temperatures, pressures,vibrations, velocities, accelerations and the like approach historicalpeaks).

In embodiments as shown in FIGS. 49 and 50 and 51 and 52, a datamonitoring system 9046 may include at least one data monitoring device9048. At least one data monitoring device 9048 may include sensors 9006and a controller 9050 comprising a data acquisition circuit 9004, asignal evaluation circuit 9008, a data storage circuit 9044, and acommunication circuit 9052 to allow data and analysis to be transmittedto a monitoring application 9056 on a remote server 9054. The signalevaluation circuit 9008 may include at least one of a peak detectioncircuit 9012. The signal evaluation circuit 9008 may periodically sharedata with the communication circuit 9052 for transmittal to the remoteserver 9054 to enable the tracking of component and equipmentperformance over time and under varying conditions by a monitoringapplication 9056. Because relevant operating conditions and/or failuremodes may occur as sensor values approach one or more criteria asdescribed elsewhere herein, the signal evaluation circuit 9008 may sharedata with the communication circuit 9052 for transmittal to the remoteserver 9054 based on the fit of data relative to one or more criteria.Based on one sensor input meeting or approaching specified criteria orrange, the signal evaluation circuit 9008 may share additional data suchas RPMs, component loads, temperatures, pressures, vibrations, and thelike for transmittal. The signal evaluation circuit 9008 may share dataat a higher data rate for transmittal to enable greater granularity inprocessing on the remote server.

In embodiments, as shown in FIG. 49, the communication circuit 9052 maycommunicate data directly to a remote server 9054. In embodiments, asshown in FIG. 50, the communication circuit 9052 may communicate data toan intermediate computer 9058 which may include a processor 9060 runningan operating system 9062 and a data storage circuit 9064.

In embodiments, as illustrated in FIGS. 51 and 52, a data collectionsystem 9066 may have a plurality of monitoring devices 9048 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.A monitoring application 9056 on a remote server 9054 may receive andstore one or more of detection values, timing signals or data comingfrom a plurality of the various monitoring devices 9048.

In embodiments, as shown in FIG. 49, the communication circuit 9052 maycommunicate data directly to a remote server 9054. In embodiments, asshown in FIG. 50, the communication circuit 9052 may communicate data toan intermediate computer 9058 which may include a processor 9060 runningan operating system 9062 and a data storage circuit 9064. There may bean individual intermediate computer 9058 associated with each monitoringdevice 9048 or an individual intermediate computer 9058 may beassociated with a plurality of monitoring devices 9048 where theintermediate computer 9058 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9054.

The monitoring application 9056 may select subsets of the detectionvalues, timing signals and data to be jointly analyzed. Subsets foranalysis may be selected based on a single type of component or a singletype of equipment in which a component is operating. Subsets foranalysis may be selected or grouped based on common operating conditionssuch as size of load, operational condition (e.g., intermittent,continuous), operating speed or tachometer, common ambient environmentalconditions such as humidity, temperature, air or fluid particulate, andthe like. Subsets for analysis may be selected based on the effects ofother nearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

The monitoring application 9056 may then analyze the selected subset. Inan illustrative example, data from a single component may be analyzedover different time periods such as one operating cycle, severaloperating cycles, a month, a year, the life of the component or thelike. Data from multiple components of the same type may also beanalyzed over different time periods. Trends in the data such as changesin frequency or amplitude may be correlated with failure and maintenancerecords associated with the same or a related component or piece ofequipment. Trends in the data, such as changing rates of changeassociated with start-up or different points in the process, may beidentified. Additional data may be introduced into the analysis such asoutput product quality, output quantity (such as per unit of time),indicated success or failure of a process, and the like. Correlation oftrends and values for different types of data may be analyzed toidentify those parameters whose short-term analysis might provide thebest prediction regarding expected performance. This information may betransmitted back to the monitoring device to update types of datacollected and analyzed locally or to influence the design of futuremonitoring devices.

In embodiments, the monitoring application 9056 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofcomponent types, operational history, historical detection values,component life models and the like for use analyzing the selected subsetusing rule-based or model-based analysis. In embodiments, the monitoringapplication 9056 may feed a neural net with the selected subset to learnto recognize peaks in waveform patterns by feeding a large data setsample of waveform behavior of a given type within which peaks aredesignated (such as by human analysts).

A monitoring system for data collection in an industrial environment,the monitoring system comprising: a data acquisition circuit structuredto interpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a peak detection circuit structured to determine at leastone peak value in response to the plurality of detection values; and apeak response circuit structured to perform at least one operation inresponse to the at least one peak value.

An example monitoring system further includes: wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one of the plurality of detection values; and a relativerate of change in amplitude and relative phase of at least one of theplurality of detection values' wherein the at least one operationcomprises issuing an alert; wherein the alert may be one of haptic,audible or visual; further comprising a data storage circuit, whereinthe relative phase difference and at least one of the detection valuesand the timing signal are stored wherein the at least one operationfurther comprises storing additional data in the data storage circuitwherein the storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference wherein the data acquisition circuit further comprises atleast one multiplexer circuit whereby alternative combinations ofdetection values may be selected based on at least one of user input anda selected operating parameter for a machine, wherein each of theplurality of detection values corresponds to at least one of the inputsensors wherein the at least one operation comprises enabling ordisabling one or more portions of the multiplexer circuit, or alteringthe multiplexer control lines wherein the data acquisition circuitcomprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits.

A monitoring system for data collection in an industrial environment,the monitoring system structure to receive input corresponding to aplurality of sensors, includes a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of the input sensors; apeak detection circuit structured to determine at least one peak valuein response to the plurality of detection values; and a peak responsecircuit structured to perform at least one operation in response to theat least one peak value.

An example monitoring system further includes: wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one of the plurality of detection values; and a relativerate of change in amplitude and relative phase of at least one of theplurality of detection values wherein the at least one operationcomprises issuing an alert wherein the alert may be one of haptic,audible or visual further comprising a data storage circuit, wherein therelative phase difference and at least one of the detection values andthe timing signal are stored wherein the at least one operation furthercomprises storing additional data in the data storage circuit whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference whereinthe data acquisition circuit further comprises at least one multiplexercircuit whereby alternative combinations of detection values may beselected based on at least one of user input and a selected operatingparameter for a machine, wherein each of the plurality of detectionvalues corresponds to at least one of the input sensors wherein the atleast one operation comprises enabling or disabling one or more portionsof the multiplexer circuit, or altering the multiplexer control lineswherein the data acquisition circuit comprises at least two multiplexercircuits and the at least one operation comprises changing connectionsbetween the at least two multiplexer circuits.

An example system for data collection, processing, and utilization ofsignals in an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a peak detection circuit structured todetermine at least one peak value in response to the plurality ofdetection values; a peak response circuit structured to select at leastone detection value in response to the at least one peak value; acommunication circuit structured to communicate the at least oneselected detection value to a remote server; and a monitoringapplication on the remote server structured to: receive the at least oneselected detection value; jointly analyze received detection values froma subset of the plurality of monitoring devices; and recommend anaction.

An example system further includes: the system further structured tosubset detection values based on one of anticipated life of a componentassociated with detection values, type of the equipment associated withdetection values, and operational conditions under which detectionvalues were measured; wherein the analysis of the subset of detectionvalues comprises feeding a neural net with the subset of detectionvalues and supplemental information to learn to recognize variousoperating states, health states, life expectancies and fault statesutilizing deep learning techniques; wherein the supplemental informationcomprises one of component specification, component performance,equipment specification, equipment performance, maintenance records,repair records and an anticipated state model wherein the at least oneoperation is further in response to at least one of: a change inamplitude of at least one of the plurality of detection values; a changein frequency or relative phase of at least one of the plurality ofdetection values; a rate of change in both amplitude and relative phaseof at least one of the plurality of detection values; and a relativerate of change in amplitude and relative phase of at least one of theplurality of detection values wherein the at least one operationcomprises issuing an alert wherein the alert may be one of haptic,audible and visual further comprising a data storage circuit, whereinthe relative phase difference and at least one of the detection valuesand the timing signal are stored wherein the at least one operationfurther comprises storing additional data in the data storage circuitwherein the storing additional data in the data storage circuit isfurther in response to at least one of: a change in the relative phasedifference and a relative rate of change in the relative phasedifference wherein the data acquisition circuit further comprises atleast one multiplexer circuit whereby alternative combinations ofdetection values may be selected based on at least one of user input anda selected operating parameter for a machine, wherein each of theplurality of detection values corresponds to at least one of the inputsensors wherein the at least one operation comprises enabling ordisabling one or more portions of the multiplexer circuit, or alteringthe multiplexer control lines and/or wherein the data acquisitioncircuit comprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits.

An example motor monitoring system, includes: a data acquisition circuitstructured to interpret a plurality of detection values from a pluralityof input sensors communicatively coupled to the data acquisitioncircuit, each of the plurality of detection values corresponding to atleast one of the input sensors; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor the motor and motor components, store historical motor performanceand buffer the plurality of detection values for a predetermined lengthof time; a peak detection circuit structured to determine a plurality ofpeak values comprising at least a temperature peak value, a speed peakvalue and a vibration peak value in response to the plurality ofdetection values and analyze the peak values relative to buffereddetection values, specifications and anticipated state informationresulting in a motor performance parameter; and a peak response circuitstructured to perform at least one operation in response to one of apeak value and a motor system performance parameter.

An example system for estimating a vehicle steering system performanceparameter, the device includes: a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe vehicle steering system, the rack, the pinion, and the steeringcolumn, store historical steering system performance and buffer theplurality of detection values for a predetermined length of time; a peakdetection circuit structured to determine a plurality of peak valuescomprising at least a temperature peak value, a speed peak value and avibration peak value in response to the plurality of detection valuesand analyze the peak values relative to buffered detection values,specifications and anticipated state information resulting in a vehiclesteering system performance parameter; and a peak response circuitstructured to perform at least one operation in response to one of apeak value and a vehicle steering system performance parameter.

An example system for estimating a pump performance parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for the pump and pumpcomponents associated with the detection values, store historical pumpperformance and buffer the plurality of detection values for apredetermined length of time; a peak detection circuit structured todetermine a plurality of peak values comprising at least a temperaturepeak value, a speed peak value and a vibration peak value in response tothe plurality of detection values and analyze the peak values relativeto buffered detection values, specifications and anticipated stateinformation resulting in a pump performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and a pump performance parameter. Incertain further embodiments, the example system includes wherein thepump is a water pump in a car and wherein the pump is a mineral pump.

An example system for estimating a drill performance parameter for adrilling machine, includes a data acquisition circuit structured tointerpret a plurality of detection values from a plurality of inputsensors communicatively coupled to the data acquisition circuit, each ofthe plurality of detection values corresponding to at least one of theinput sensors; a data storage circuit structured to storespecifications, system geometry, and anticipated state information forthe drill and drill components associated with the detection values,store historical drill performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a drill performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a drill performanceparameter. An example system further includes wherein the drillingmachine is one of an oil drilling machine and a gas drilling machine.

An example system for estimating a conveyor health parameter, the systemincludes: a data acquisition circuit structured to interpret a pluralityof detection values from a plurality of input sensors communicativelycoupled to the data acquisition circuit, each of the plurality ofdetection values corresponding to at least one of the input sensors; adata storage circuit structured to store specifications, systemgeometry, and anticipated state information for a conveyor and conveyorcomponents associated with the detection values, store historicalconveyor performance and buffer the plurality of detection values for apredetermined length of time; a peak detection circuit structured todetermine a plurality of peak values comprising at least a temperaturepeak value, a speed peak value and a vibration peak value in response tothe plurality of detection values and analyze the peak values relativeto buffered detection values, specifications and anticipated stateinformation resulting in a conveyor performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and a conveyor performance parameter.

An example system for estimating an agitator health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for an agitator andagitator components associated with the detection values, storehistorical agitator performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in an agitator performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and an agitatorperformance parameter. In certain embodiments, a system further includeswhere the agitator is one of a rotating tank mixer, a large tank mixer,a portable tank mixer, a tote tank mixer, a drum mixer, a mounted mixerand a propeller mixer.

An example system for estimating a compressor health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for a compressor andcompressor components associated with the detection values, storehistorical compressor performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a compressor performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a compressorperformance parameter.

An example system for estimating an air conditioner health parameter,the system includes: a data acquisition circuit structured to interpreta plurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for an airconditioner and air conditioner components associated with the detectionvalues, store historical air conditioner performance and buffer theplurality of detection values for a predetermined length of time; a peakdetection circuit structured to determine a plurality of peak valuescomprising at least a temperature peak value, a speed peak value, apressure value and a vibration peak value in response to the pluralityof detection values and analyze the peak values relative to buffereddetection values, specifications and anticipated state informationresulting in an air conditioner performance parameter; and a peakresponse circuit structured to perform at least one operation inresponse to one of a peak value and an air conditioner performanceparameter.

An example system for estimating a centrifuge health parameter, thesystem includes: a data acquisition circuit structured to interpret aplurality of detection values from a plurality of input sensorscommunicatively coupled to the data acquisition circuit, each of theplurality of detection values corresponding to at least one of the inputsensors; a data storage circuit structured to store specifications,system geometry, and anticipated state information for a centrifuge andcentrifuge components associated with the detection values, storehistorical centrifuge performance and buffer the plurality of detectionvalues for a predetermined length of time; a peak detection circuitstructured to determine a plurality of peak values comprising at least atemperature peak value, a speed peak value and a vibration peak value inresponse to the plurality of detection values and analyze the peakvalues relative to buffered detection values, specifications andanticipated state information resulting in a centrifuge performanceparameter; and a peak response circuit structured to perform at leastone operation in response to one of a peak value and a centrifugeperformance parameter.

Bearings are used throughout many different types of equipment andapplications. Bearings may be present in or supporting shafts, motors,rotors, stators, housings, frames, suspension systems and components,gears, gear sets of various types, other bearings, and other elements.Bearings may be used as support for high speed vehicles such as maglevtrains. Bearings are used to support rotating shafts for engines,motors, generators, fans, compressors, turbines and the like. Giantroller bearings may be used to support buildings and physicalinfrastructure. Different types of bearings may be used to supportconventional, planetary and other types of gears. Bearings may be usedto support transmissions and gear boxes such as roller thrust bearings,for example. Bearings may be used to support wheels, wheel hubs andother rolling parts using tapered roller bearings.

There are many different types of bearings such as roller bearings,needle bearings, sleeve bearings, ball bearings, radial bearings, thrustload bearings including ball thrust bearings used in low speedapplications and roller thrust bearings, taper bearings and taperedroller bearings, specialized bearings, magnetic bearings, giant rollerbearings, jewel bearings (e.g., Sapphire), fluid bearings, flexurebearings to support bending element loads, and the like. References tobearings throughout this disclosure is intended to include, but not belimited by, the terms listed above.

In embodiments, information about the health or other status or stateinformation of or regarding a bearing in a piece of industrial equipmentor in an industrial process may be obtained by monitoring the conditionof various components of the industrial equipment or industrial process.Monitoring may include monitoring the amplitude and/or frequency and/orphase of a sensor signal measuring attributes such as temperature,humidity, acceleration, displacement and the like.

An embodiment of a data monitoring device 9200 is shown in FIG. 53 andmay include a plurality of sensors 9206 communicatively coupled to acontroller 9202. The controller 9202 may include a data acquisitioncircuit 9204, a data storage circuit 9216, a signal evaluation circuit9208 and, optionally, a response circuit 9210. The signal evaluationcircuit 9208 may comprise a frequency transformation circuit 9212 and afrequency evaluation circuit 9214.

The plurality of sensors 9206 may be wired to ports 9226 (reference FIG.54) on the data acquisition circuit 9204. The plurality of sensors 9206may be wirelessly connected to the data acquisition circuit 9204. Thedata acquisition circuit 9204 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9206 where the sensors 9206 may be capturing data on differentoperational aspects of a bearing or piece of equipment orinfrastructure.

The selection of the plurality of sensors 9206 for a data monitoringdevice 9200 designed for a specific bearing or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, reliability of thesensors, and the like. The impact of failure may drive the extent towhich a bearing or piece of equipment is monitored with more sensorsand/or higher capability sensors being dedicated to systems whereunexpected or undetected bearing failure would be costly or have severeconsequences.

The signal evaluation circuit 9208 may process the detection values toobtain information about a bearing being monitored. The frequencytransformation circuit 9212 may transform one or more time-baseddetection values to frequency information. The transformation may beaccomplished using techniques such as a digital Fast Fourier transform(“FFT”), Laplace transform, Z-transform, wavelet transform, otherfrequency domain transform, or other digital or analog signal analysistechniques, including, without limitation, complex analysis, includingcomplex phase evolution analysis.

The frequency evaluation circuit 9214 (or frequency analysis circuit)may be structured to detect signals at frequencies of interest.Frequencies of interest may include frequencies higher than thefrequency at which the equipment rotates (as measured by a tachometer,for instance), various harmonics and/or resonant frequencies associatedwith the equipment design and operating conditions such as multiples ofshaft rotation velocities or other rotating components for the equipmentthat is borne by the bearings. Changes in energy at frequencies close tothe operating frequency may be an indicator of balance/imbalance in thesystem. Changes in energy at frequencies on the order of twice theoperating frequency may be indicative of a system misalignment—forexample, on the coupling, or a looseness in the system, (e.g., rattlingat harmonics of the operating frequency). Changes in energy atfrequencies close to three or four times the operating frequency,corresponding to the number of bolts on a coupling, may indicate wear ofon one of the couplings. Changes in energy at frequencies of four, five,or more times the operating frequency may relate back to something thathas a corresponding number of elements, such as if there are energypeaks or activity around five times the operating frequency there may bewear or an imbalance in a five-vane pump or the like.

In an illustrative and non-limiting example, in the analysis of rollerbearings, frequencies of interest may include ball spin frequencies,cage spin frequencies, inner race frequency (as bearings often sit on arace inside a cage), outer race frequency and the like. Bearings thatare damaged or beginning to fail may show humps of energy at thefrequencies mentioned above and elsewhere in this disclosure. The energyat these frequencies may increase over time as the bearings wear moreand become more damaged due to more variations in rotationalacceleration and pings.

In an illustrative and non-limiting example, bad bearings may show humpsof energy and the intensity of high frequency measurements may start togrow over time as bearings wear and become imperfect (greateracceleration and pings may show up in high frequency measurementdomains). Those measurements may be indicators of air gaps in thebearing system. As bearings begin to wear, harder hits may cause theenergy signal to move to higher frequencies.

In embodiments, the signal evaluation circuit 9208 may also include oneor more of a phase detection circuit, a phase lock loop circuit, abandpass filter circuit, a peak detection circuit, and the like.

In embodiments, the signal evaluation circuit 9208 may include atransitory signal analysis circuit. Transient signals may cause smallamplitude vibrations. However, the challenge in bearing analysis is thatyou may receive a signal associated with a single or non-periodic impactand an exponential decay. Thus, the oscillation of the bearing may notbe represented by a single sine wave, but rather by a spectrum of manyhigh frequency sine waves. For example, a signal from a failing bearingmay only be seen, in a time-based signal, as a low amplitude spike for ashort amount of time. A signal from a failing bearing may be lower inamplitude than a signal associated with an imbalance even though theconsequences of a failed bearing may be more significant. It isimportant to be able to identify these signals. This type of lowamplitude, transient signal may be best analyzed using transientanalysis rather than a conventional frequency transformation, such as anFFT, which would treat the signal like a low frequency sine wave. Ahigher resolution data stream may also provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component operating at low RPMs.

In embodiments, the transitory signal analysis circuit for bearinganalysis may include envelope modulation analysis and other transitorysignal analysis techniques. The signal evaluation circuit 9208 may storelong stream of detection values to the data storage circuit 9216. Thetransitory signal analysis circuit may use envelope analysis techniqueson those long streams of detection values to identify transient effects(such as impacts) which may not be identified by conventional sine waveanalysis (such as FFTs).

The signal evaluation circuit 9208 may utilize transitory signalanalysis models optimized for the type of component being measured suchas bearings, gears, variable speed machinery and the like. In anillustrative and non-limiting example, a gear may resonate close to itsaverage rotational speed. In an illustrative and non-limiting example, abearing may resonate close to the bearing rotation frequency and producea ringing in amplitude around that frequency. For example, if the shaftinner race is wearing there may be chatter between the inner race andthe shaft resulting in amplitude modulation to the left and right of thebearing frequency. The amplitude modulation may demonstrate its own sinewave characteristics with its own side bands. Various signal processingtechniques may be used to eliminate the sinusoidal component, resultingin a modulation envelope for analysis.

The signal evaluation circuit 9208 may be optimized for variable speedmachinery. Historically, variable speed machinery was expensive to make,and it was common to use DC motors and variable sheaves, such that flowcould be controlled using vanes. Variable speed motors became morecommon with solid-state drive advances (“SCR devices”). The baseoperating frequency of equipment may be varied from the 50-60 Hzprovided by standard utility companies and either and slowed down orsped up to run the equipment at different speeds depending on theapplication. The ability to run the equipment at varying speeds mayresult in energy savings. However, depending on the equipment geometry,there may be some speeds which create vibrations at resonantfrequencies, reducing the life of the components. Variable speed motorsmay also emit electricity into bearings which may damage the bearings.In embodiments, the analysis of long data streams for envelopemodulation analysis and other transitory signal analysis techniques asdescribed herein may be useful in identifying these frequencies suchthat control schemes for the equipment may be designed to avoid thosespeeds which result in unacceptable vibrations and/or damage to thebearings.

In an illustrative and non-limiting example, heating, ventilation andair conditioning (“HVAC”) systems may be assembled on site usingvariable speed motors, fans, belts, compressors and the like where theoperating speeds are not constant, and their relative relationships areunknown. In an illustrative and non-limiting example, variable speedmotors may be used in fan pumps for building air circulation. Variablespeed motors may be used to vary the speed of conveyors—for example, inmanufacturing assembly lines or steel mills. Variable speed motors maybe used for fans in a pharmaceutical process, such as where it may becritical to avoid vibration.

In an illustrative and non-limiting example, sleeve bearings may beanalyzed for defects. Sleeve bearings typically have an oil system. Ifthe oil flow stops or the oil becomes severely contaminated, failure canoccur very quickly. Therefore, a fluid particulate sensor or fluidpressure sensors may be an important source of detection values.

In an illustrative and non-limiting example, fan integrity may beevaluated by measuring air pulsations related to blade pass frequencies.For example, if a fan has 12 blades, 12 air pulsations may be measured.Variations in the amplitude of the pulsations associated with thedifferent blades may be indicative of changes in a fan blade. Changes infrequencies associated with the air pulsations may be indicative ofbearing problems.

In an illustrative and non-limiting example, compressors used in the gasand oil field or in gas handling equipment on an assembly line may beevaluated by measuring the periodic increases in energy/pressure in thestorage vessel as gas is pumped into the vessel. Periodic variations inthe amplitude of the energy increases may be associated with piston wearor damage to a portion of a rotary screw. Phase evaluation of the energysignal relative to timing signals may be helpful in identifying whichpiston or portion of the rotary screw has damage. Changes in frequenciesassociated with the energy pulsations may be indicative of bearingproblems.

In an illustrative and non-limiting example, cavitation/air pockets inpumps may create shuttering in the pump housing and the output flowwhich may be identified with the frequency transformation and frequencyanalysis techniques described above and elsewhere herein.

In an illustrative and non-limiting example, the frequencytransformation and frequency analysis techniques described above andelsewhere herein may assist in the identification of problems incomponents of building HVAC systems such as big fans. If the dampers ofthe system are set poorly it may result in ducts pulsing or vibrating asair is pushed through the system. Monitoring of vibration sensors on theducts may assist in the balancing of the system. If there are defects inthe blades of the big fan this may also result in uneven air flow andresulting pulsation in the buildings ductwork.

In an illustrative and non-limiting example, detection values fromacoustical sensors located close to the bearings may assist in theidentification of issues in the engagement between gears or badbearings. Based on a knowledge of gear ratios, such as the “in” and“out” gear ratios, for a system and measurements of the input and outputrotational speed, detection values may be evaluated for energy occurringat those ratios, which in turn may be used to identify bad bearings.This could be done with simple off the shelf motors rather thanrequiring extensive retrofitting of the motor with sensors.

Based on the output of its various components, the signal evaluationcircuit 9208 may make a bearing life prediction, identify a bearinghealth parameter, identify a bearing performance parameter, determine abearing health parameter (e.g., fault conditions), and the like. Thesignal evaluation circuit 9208 may identify wear on a bearing, identifythe presence of foreign matter (e.g., particulates) in the bearings,identify air gaps or a loss of fluid in oil/fluid coated bearings,identify a loss of lubrication in a set of bearings, identify a loss ofpower for magnetic bearings and the like, identify strain/stress offlexure bearings, and the like. The signal evaluation circuit 9208 mayidentify optimal operation parameters for a piece of equipment to extendbearing life. The signal evaluation circuit 9208 may identify behavior(resonant wobble) at a selected operational frequency (e.g., shaftrotation rate).

The signal evaluation circuit 9208 may communicate with the data storagecircuit 9216 to access equipment specifications, equipment geometry,bearing specifications, bearing materials, anticipated state informationfor a plurality of bearing types, operational history, historicaldetection values, and the like for use in assessing the output of itsvarious components. The signal evaluation circuit 9208 may buffer asubset of the plurality of detection values, intermediate data such astime-based detection values transformed to frequency information,filtered detection values, identified frequencies of interest, and thelike for a predetermined length of time. The signal evaluation circuit9208 may periodically store certain detection values in the data storagecircuit 9216 to enable the tracking of component performance over time.In embodiments, based on relevant operating conditions and/or failuremodes that may occur as detection values approach one or more criteria,the signal evaluation circuit 9208 may store data in the data storagecircuit 9216 based on the fit of data relative to one or more criteria,such as those described throughout this disclosure. Based on one sensorinput meeting or approaching specified criteria or range, the signalevaluation circuit 9208 may store additional data such as RPMs,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure in the datastorage circuit 9216. The signal evaluation circuit 9208 may store dataat a higher data rate for greater granularity in future processing, theability to reprocess at different sampling rates, and/or to enablediagnosing or post-processing of system information where operationaldata of interest is flagged, and the like.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors9206 may comprise one or more of, without limitation, a vibrationsensor, an optical vibration sensor, a thermometer, a hygrometer, avoltage sensor, a current sensor, an accelerometer, a velocity detector,a light or electromagnetic sensor (e.g., determining temperature,composition and/or spectral analysis, and/or object position ormovement), an image sensor, a structured light sensor, a laser-basedimage sensor, an infrared sensor, an acoustic wave sensor, a heat fluxsensor, a displacement sensor, a turbidity meter, a viscosity meter, aload sensor, a tri-axial vibration sensor, an accelerometer, atachometer, a fluid pressure meter, an air flow meter, a horsepowermeter, a flow rate meter, a fluid particle detector, an acousticalsensor, a pH sensor, and the like, including, without limitation, any ofthe sensors described throughout this disclosure and the documentsincorporated by reference. The sensors may typically comprise at least atemperature sensor, a load sensor, a tri-axial sensor and a tachometer.

The sensors 9206 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9206 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9206 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

In embodiments, as illustrated in FIG. 53, the sensors 9206 may be partof the data monitoring device 9200, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 54 and 55, oneor more external sensors 9224, which are not explicitly part of amonitoring device 9218 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9218. The monitoringdevice 9218 may include a controller 9220. The controller 9202 mayinclude a data acquisition circuit 9222, a data storage circuit 9216, asignal evaluation circuit 9208 and, optionally, a response circuit 9210.The signal evaluation circuit 9208 may comprise a frequencytransformation circuit 9212 and a frequency analysis circuit 9214. Thedata acquisition circuit 9222 may include one or more input ports 9226.The one or more external sensors 9224 may be directly connected to theone or more input ports 9226 on the data acquisition circuit 9222 of thecontroller 9220 or may be accessed by the data acquisition circuit 9222wirelessly, such as by a reader, interrogator, or other wirelessconnection, such as over a short-distance wireless protocol. Inembodiments as shown in FIG. 55, a data acquisition circuit 9222 mayfurther comprise a wireless communications circuit 9262. The dataacquisition circuit 9222 may use the wireless communications circuit9262 to access detection values corresponding to the one or moreexternal sensors 9224 wirelessly or via a separate source or somecombination of these methods.

In embodiments, as illustrated in FIG. 56, the data acquisition circuit9222 may further comprise a multiplexer circuit 9236 as describedelsewhere herein. Outputs from the multiplexer circuit 9236 may beutilized by the signal evaluation circuit 9208. The response circuit9210 may have the ability to turn on and off portions of the multiplexorcircuit 9236. The response circuit 9210 may have the ability to controlthe control channels of the multiplexor circuit 9236.

The response circuit 9210 may initiate actions based on a bearingperformance parameter, a bearing health value, a bearing life predictionparameter, and the like. The response circuit 9210 may evaluate theresults of the signal evaluation circuit 9208 and, based on certaincriteria or the output from various components of the signal evaluationcircuit 9208, initiate an action. The criteria may include a sensor'sdetection values at certain frequencies or phases relative to a timersignal where the frequencies or phases of interest may be based on theequipment geometry, equipment control schemes, system input, historicaldata, current operating conditions, and/or an anticipated response. Thecriteria may include a sensor's detection values at certain frequenciesor phases relative to detection values of a second sensor. The criteriamay include signal strength at certain resonant frequencies/harmonicsrelative to detection values associated with a system tachometer oranticipated based on equipment geometry and operation conditions.Criteria may include a predetermined peak value for a detection valuefrom a specific sensor, a cumulative value of a sensor's correspondingdetection value over time, a change in peak value, a rate of change in apeak value, and/or an accumulated value (e.g., a time spent above/belowa threshold value, a weighted time spent above/below one or morethreshold values, and/or an area of the detected value above/below oneor more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like. The relative criteria may be reflected inone or more calculated statistics or metrics (including ones generatedby further calculations on multiple criteria or statistics), which inturn may be used for processing (such as on-board a data collector or byan external system), such as to be provided as an input to one or moreof the machine learning capabilities described in this disclosure, to acontrol system (which may be on board a data collector or remote, suchas to control selection of data inputs, multiplexing of sensor data,storage, or the like), or as a data element that is an input to anothersystem, such as a data stream or data package that may be available to adata marketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example, where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. For example, and withoutlimitation, vibrational data may indicate system agitation levels,properly operating equipment, or the like, and vibrational data belowamplitude and/or frequency thresholds may be an indication of a processthat is not operating according to expectations. Except where thecontext clearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated systemresponse or vibration based on the equipment geometry and control schemesuch as number of bearings, relative rotational speed, influx of powerto the system at a certain frequency, and the like. The predeterminedacceptable range may also be based on long term analysis of detectionvalues across a plurality of similar equipment and components andcorrelation of data with equipment failure.

In some embodiments, an alert may be issued based on some of thecriteria discussed above. In an illustrative example, an increase intemperature and energy at certain frequencies may indicate a hot bearingthat is starting to fail. In embodiments, the relative criteria for analarm may change with other data or information such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 9210 mayinitiate an alert if a vibrational amplitude and/or frequency exceeds apredetermined maximum value, if there is a change or rate of change thatexceeds a predetermined acceptable range, and/or if an accumulated valuebased on vibrational amplitude and/or frequency exceeds a threshold.

In embodiments, response circuit 9210 may cause the data acquisitioncircuit 9204 to enable or disable the processing of detection valuescorresponding to certain sensors based on some of the criteria discussedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like, or accessing new sensors ortypes of sensors, and the like. Switching may be undertaken based on amodel, a set of rules, or the like. In embodiments, switching may beunder control of a machine learning system, such that switching iscontrolled based on one or more metrics of success, combined with inputdata, over a set of trials, which may occur under supervision of a humansupervisor or under control of an automated system. Switching mayinvolve switching from one input port to another (such as to switch fromone sensor to another). Switching may involve altering the multiplexingof data, such as combining different streams under differentcircumstances. Switching may also involve activating a system to obtainadditional data, such as moving a mobile system (such as a robotic ordrone system), to a location where different or additional data isavailable (such as positioning an image sensor for a different view orpositioning a sonar sensor for a different direction of collection) orto a location where different sensors can be accessed (such as moving acollector to connect up to a sensor that is disposed at a location in anenvironment by a wired or wireless connection). This switching may beimplemented by changing the control signals for a multiplexor circuit9236 and/or by turning on or off certain input sections of themultiplexor circuit 9236. The response circuit 9210 may makerecommendations for the replacement of certain sensors in the futurewith sensors having different response rates, sensitivity, ranges, andthe like. The response circuit 9210 may recommend design alterations forfuture embodiments of the component, the piece of equipment, theoperating conditions, the process, and the like.

In embodiments, the response circuit 9210 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 9210 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 9210 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments as shown in FIGS. 57, 58, 59, and 60, a data monitoringsystem 9240 may include at least one data monitoring device 9250. The atleast one data monitoring device 9250 may include sensors 9206 and acontroller 9242 comprising a data acquisition circuit 9204, a signalevaluation circuit 9208, a data storage circuit 9216, and acommunications circuit 9246. The signal evaluation circuit 9208 mayinclude at least one of a frequency detection circuit 9212 and afrequency analysis circuit 9214. There may also be an optional responsecircuit as described above and elsewhere herein. The signal evaluationcircuit 9208 may periodically share data with the communication circuit9246 for transmittal to a remote server 9244 to enable the tracking ofcomponent and equipment performance over time and under varyingconditions by a monitoring application 9248. Because relevant operatingconditions and/or failure modes may occur as sensor values approach oneor more criteria, the signal evaluation circuit 9208 may share data withthe communication circuit 9246 for transmittal to the remote server 9244based on the fit of data relative to one or more criteria. Based on onesensor input meeting or approaching specified criteria or range, thesignal evaluation circuit 9208 may share additional data such as RPMs,component loads, temperatures, pressures, vibrations, and the like fortransmittal. The signal evaluation circuit 9208 may share data at ahigher data rate for transmittal to enable greater granularity inprocessing on the remote server.

In embodiments, as shown in FIG. 57, the communications circuit 9246 maycommunicate data directly to a remote server 9244. In embodiments, asshown in FIG. 58, the communications circuit 9246 may communicate datato an intermediate computer 9252, which may include a processor 9254running an operating system 9256 and a data storage circuit 9258. Theintermediate computer 9252 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9244.

In embodiments, as illustrated in FIGS. 59 and 60, a data collectionsystem 9260 may have a plurality of monitoring devices 9250 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment,(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.A monitoring application 9248 on a remote server 9244 may receive andstore one or more of the following: detection values, timing signals anddata coming from a plurality of the various monitoring devices 9250. Inembodiments, as shown in FIG. 59, the communications circuit 9246 maycommunicate data directly to a remote server 9244. In embodiments, asshown in FIG. 60, the communications circuit 9246 may communicate datato an intermediate computer 9252, which may include a processor 9254running an operating system 9256 and a data storage circuit 9258. Theremay be an individual intermediate computer 9252 associated with eachmonitoring device 9264 or an individual intermediate computer 9252 maybe associated with a plurality of monitoring devices 9250 where theintermediate computer 9252 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9244.

The monitoring application 9248 may select subsets of the detectionvalues, timing signals and data to be jointly analyzed. Subsets foranalysis may be selected based on a bearing type, bearing materials, ora single type of equipment in which a bearing is operating. Subsets foranalysis may be selected or grouped based on common operating conditionsor operational history such as size of load, operational condition(e.g., intermittent, continuous), operating speed or tachometer, commonambient environmental conditions such as humidity, temperature, air orfluid particulate, and the like. Subsets for analysis may be selectedbased on common anticipated state information. Subsets for analysis maybe selected based on the effects of other nearby equipment such asnearby machines rotating at similar frequencies, nearby equipmentproducing electromagnetic fields, nearby equipment producing heat,nearby equipment inducing movement or vibration, nearby equipmentemitting vapors, chemicals or particulates, or other potentiallyinterfering or intervening effects.

The monitoring application 9248 may analyze a selected subset. In anillustrative example, data from a single component may be analyzed overdifferent time periods, such as one operating cycle, cycle-to-cyclecomparisons, trends over several operating cycles/times such as a month,a year, the life of the component, or the like. Data from multiplecomponents of the same type may also be analyzed over different timeperiods. Trends in the data such as changes in frequency or amplitudemay be correlated with failure and maintenance records associated withthe same component or piece of equipment. Trends in the data such aschanging rates of change associated with start-up or different points inthe process may be identified. Additional data may be introduced intothe analysis such as output product quality, output quantity (such asper unit of time), indicated success or failure of a process, and thelike. Correlation of trends and values for different types of data maybe analyzed to identify those parameters whose short-term analysis mightprovide the best prediction regarding expected performance. The analysismay identify model improvements to the model for anticipated stateinformation, recommendations around sensors to be used, positioning ofsensors and the like. The analysis may identify additional data tocollect and store. The analysis may identify recommendations regardingneeded maintenance and repair and/or the scheduling of preventativemaintenance. The analysis may identify recommendations around purchasingreplacement bearings and the timing of the replacement of the bearings.The analysis may result in warning regarding the dangers of catastrophicfailure conditions. This information may be transmitted back to themonitoring device to update types of data collected and analyzed locallyor to influence the design of future monitoring devices.

In embodiments, the monitoring application 9248 may have access toequipment specifications, equipment geometry, bearing specifications,bearing materials, anticipated state information for a plurality ofbearing types, operational history, historical detection values, bearinglife models and the like for use analyzing the selected subset usingrule-based or model-based analysis. In embodiments, the monitoringapplication 9248 may feed a neural net with the selected subset to learnto recognize various operating state, health states (e.g., lifetimepredictions) and fault states utilizing deep learning techniques. Inembodiments, a hybrid of the two techniques (model-based learning anddeep learning) may be used.

In an illustrative and non-limiting example, the health of bearings onconveyors and lifters in an assembly line, in water pumps on industrialvehicles and in compressors in gas handling systems, in compressorssituated out in the gas and oil fields, in factory air conditioningunits and in factory mineral pumps may be monitored using the frequencytransformation and frequency analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of one or moreof bearings, gears, blades, screws and associated shafts, motors,rotors, stators, gears, and other components of gear boxes, motors,pumps, vibrating conveyors, mixers, centrifuges, drilling machines,screw drivers and refining tanks situated in the oil and gas fields maybe evaluated using the frequency transformation and frequency analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof rotating tank/mixer agitators, mechanical/rotating agitators, andpropeller agitators, to promote chemical reactions deployed in chemicaland pharmaceutical production lines may be evaluated using the frequencytransformation and frequency analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle systems such as steering mechanisms or engines may beevaluated using the frequency transformation and frequency analysistechniques, data monitoring devices and data collection systemsdescribed herein.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter.

In certain further embodiments, an example monitoring device includesone or more of: a response circuit to perform at least one operation inresponse to the bearing performance parameter, wherein the plurality ofinput sensors includes at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor and a tachometer; whereinthe at least one operation is further in response to at least one of: achange in amplitude of at least one of the plurality of detectionvalues; a change in frequency or relative phase of at least one of theplurality of detection values; a rate of change in both amplitude andrelative phase of at least one of the plurality of detection values; anda relative rate of change in amplitude and relative phase of at leastone of the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; wherein the at least one operation furthercomprises storing additional data in the data storage circuit; whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference.

An example monitoring device for bearing analysis in an industrialenvironment, the monitoring device includes: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing specifications andanticipated state information for a plurality of bearing types andbuffering the plurality of detection values for a predetermined lengthof time; and a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing health value.

In certain embodiments, an example monitoring device further includesone or more of: a response circuit to perform at least one operation inresponse to the bearing health value, wherein the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer; whereinthe at least one operation is further in response to at least one of: achange in amplitude of at least one of the plurality of detectionvalues; a change in frequency or relative phase of at least one of theplurality of detection values; a rate of change in both amplitude andrelative phase of at least one of the plurality of detection values; anda relative rate of change in amplitude and relative phase of at leastone of the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; wherein the at least one operation furthercomprises storing additional data in the data storage circuit; whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearinglife prediction parameter.

In certain embodiments, a monitoring device further includes one or moreof: a response circuit to perform at least one operation in response tothe bearing life prediction parameter, wherein the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer; whereinthe at least one operation is further in response to at least one of: achange in amplitude of at least one of the plurality of detectionvalues; a change in frequency or relative phase of at least one of theplurality of detection values; a rate of change in both amplitude andrelative phase of at least one of the plurality of detection values; anda relative rate of change in amplitude and relative phase of at leastone of the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; wherein the at least one operation furthercomprises storing additional data in the data storage circuit; whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference.

An example monitoring device for bearing analysis in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time; and a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter, wherein the data acquisition circuit comprises amultiplexer circuit whereby alternative combinations of the detectionvalues may be selected based on at least one of user input, a detectedstate and a selected operating parameter for a machine.

In certain further embodiments, an example monitoring device furtherincludes one or more of: a response circuit to perform at least oneoperation in response to the bearing performance parameter, wherein theplurality of input sensors includes at least two sensors selected fromthe group consisting of a temperature sensor, a load sensor, an opticalvibration sensor, an acoustic wave sensor, a heat flux sensor, aninfrared sensor, an accelerometer, a tri-axial vibration sensor and atachometer; a change in amplitude of at least one of the plurality ofdetection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one of the plurality ofdetection values; and a relative rate of change in amplitude andrelative phase of at least one of the plurality of detection values;wherein the at least one operation comprises issuing an alert; whereinthe alert may be one of haptic, audible and visual; wherein the at leastone operation further comprises storing additional data in the datastorage circuit; wherein the storing additional data in the data storagecircuit is further in response to at least one of: a change in therelative phase difference and a relative rate of change in the relativephase difference; wherein the at least one operation comprises enablingor disabling one or more portions of the multiplexer circuit, oraltering the multiplexer control lines; wherein the data acquisitioncircuit comprises at least two multiplexer circuits and the at least oneoperation comprises changing connections between the at least twomultiplexer circuits.

An example system for data collection, processing, and bearing analysisin an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing specifications andanticipated state information for a plurality of bearing types andbuffering the plurality of detection values for a predetermined lengthof time;

a bearing analysis circuit structured to analyze buffered detectionvalues relative to specifications and anticipated state informationresulting in a bearing life prediction; a communication circuitstructured to communicate with a remote server providing the bearinglife prediction and a portion of the buffered detection values to theremote server; and a monitoring application on the remote serverstructured to receive, store and jointly analyze a subset of thedetection values from the plurality of monitoring devices.

In certain further embodiments, an example monitoring device includesone or more of: a response circuit to perform at least one operation inresponse to the bearing life prediction, wherein the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer; whereinthe at least one operation is further in response to at least one of: achange in amplitude of at least one of the plurality of detectionvalues; a change in frequency or relative phase of at least one of theplurality of detection values; a rate of change in both amplitude andrelative phase of at least one of the plurality of detection values; anda relative rate of change in amplitude and relative phase of at leastone of the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; wherein the at least one operation furthercomprises storing additional data in the data storage circuit; whereinthe storing additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference.

An example system for data collection, processing, and bearing analysisin an industrial environment comprising: a plurality of monitoringdevices, each comprising: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing specifications and anticipated state information fora plurality of bearing types and buffering the plurality of detectionvalues for a predetermined length of time;

a bearing analysis circuit structured to analyze buffered detectionvalues relative to specifications and anticipated state informationresulting in a bearing performance parameter; a communication circuitstructured to communicate with a remote server providing the lifeprediction and a portion of the buffered detection values to the remoteserver; and a monitoring application on the remote server structured toreceive, store and jointly analyze a subset of the detection values fromthe plurality of monitoring devices.

In certain further embodiments, an example monitoring device furtherincludes one or more of: a response circuit to perform at least oneoperation in response to the bearing performance parameter, wherein theplurality of input sensors includes at least two sensors selected fromthe group consisting of a temperature sensor, a load sensor, an opticalvibration sensor, an acoustic wave sensor, a heat flux sensor, aninfrared sensor, an accelerometer, a tri-axial vibration sensor and atachometer; wherein the at least one operation is further in response toat least one of: a change in amplitude of at least one of the pluralityof detection values; a change in frequency or relative phase of at leastone of the plurality of detection values; a rate of change in bothamplitude and relative phase of at least one the plurality of detectionvalues; and a relative rate of change in amplitude and relative phase ofat least one the plurality of detection values; wherein the at least oneoperation comprises issuing an alert; wherein the alert may be one ofhaptic, audible and visual; wherein the at least one operation furthercomprises storing additional data in the data storage circuit; whereinstoring additional data in the data storage circuit is further inresponse to at least one of: a change in the relative phase differenceand a relative rate of change in the relative phase difference.

An example system for data collection, processing, and bearing analysisin an industrial environment includes: a plurality of monitoringdevices, each monitoring device comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a streaming circuit for streaming at least a subsetof the acquired detection values to a remote learning system; and aremote learning system including a bearing analysis circuit structuredto analyze the detection values relative to a machine-basedunderstanding of the state of the at least one bearing.

In certain further embodiments, an example system further includes oneor more of: wherein the machine-based understanding is developed basedon a model of the bearing that determines a state of the at least onebearing based at least in part on the relationship of the behavior ofthe bearing to an operating frequency of a component of the industrialmachine; wherein the state of the at least one bearing is at least oneof an operating state, a health state, a predicted lifetime state and afault state; wherein the machine-based understanding is developed basedby providing inputs to a deep learning machine, wherein the inputscomprise a plurality of streams of detection values for a plurality ofbearings and a plurality of measured state values for the plurality ofbearings; wherein the state of the at least one bearing is at least oneof an operating state, a health state, a predicted lifetime state and afault state.

An example method of analyzing bearings and sets of bearings, includes:receiving a plurality of detection values corresponding to data from atemperature sensor, a vibration sensor positioned near the bearing orset of bearings and a tachometer to measure rotation of a shaftassociated with the bearing or set of bearings; comparing the detectionvalues corresponding to the temperature sensor to a predeterminedmaximum level; filtering the detection values corresponding to thevibration sensor through a high pass filter where the filter is selectedto eliminate vibrations associated with detection values associated withthe tachometer; identifying rapid changes in at least one of atemperature peak and a vibration peak; identifying frequencies at whichspikes in the filtered detection values corresponding to the vibrationsensor occur and comparing frequencies and spikes in amplitude relativeto an anticipated state information and specification associated withthe bearing or set of bearings; and determining a bearing healthparameter.

An example device for monitoring roller bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage circuit structured to store specifications and anticipated stateinformation for a plurality of types of roller bearings and bufferingthe plurality of detection values for a predetermined length of time; abearing analysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a bearing performance parameter; and

a response circuit to perform at least one operation in response to thebearing performance prediction, wherein the plurality of input sensorsincludes at least two sensors selected from the group consisting of atemperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example device for monitoring sleeve bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing sleeve bearing specifications and anticipated stateinformation for types of sleeve bearings and buffering the plurality ofdetection values for a predetermined length of time; a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter; and

a response circuit to perform at least one operation in response to thebearing performance parameter, wherein the plurality of input sensorsincludes at least two sensors selected from the group consisting of atemperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example system for monitoring pump bearings in an industrialenvironment, includes: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to at least one of a plurality of inputsensors communicatively coupled to the data acquisition circuit; a datastorage for storing pump specifications, bearing specifications,anticipated state information for pump bearings and buffering theplurality of detection values for a predetermined length of time; abearing analysis circuit structured to analyze buffered detection valuesrelative to specifications and anticipated state information resultingin a bearing performance parameter; and

a response circuit to perform at least one operation in response to thebearing performance parameter, wherein the plurality of input sensorsincludes at least two sensors selected from the group consisting of atemperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor and a tachometer.

An example system for collection, processing, and analyzing pumpbearings in an industrial environment includes: a plurality ofmonitoring devices, each comprising: a data acquisition circuitstructured to interpret a plurality of detection values, each of theplurality of detection values corresponding to at least one of aplurality of input sensors communicatively coupled to the dataacquisition circuit; a data storage for storing pump specifications,bearing specifications, anticipated state information for pump bearingsand buffering the plurality of detection values for a predeterminedlength of time; a bearing analysis circuit structured to analyzebuffered detection values relative to the pump and bearingspecifications and anticipated state information resulting in a bearingperformance parameter; a communication circuit structured to communicatewith a remote server providing the bearing performance parameter and aportion of the buffered detection values to the remote server; and amonitoring application on the remote server structured to receive, storeand jointly analyze a subset of the detection values from the pluralityof monitoring devices.

An example system for estimating a conveyor health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for the conveyor and associated rotatingcomponents, store historical conveyor and component performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and

a system analysis circuit structured to utilize the bearing performanceand at least one of an anticipated state, historical data and a systemgeometry to estimate a conveyor health performance.

An example system for estimating an agitator health parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for the agitator and associatedcomponents, store historical agitator and component performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and a systemanalysis circuit structured to utilize the bearing performance and atleast one of an anticipated state, historical data and a system geometryto estimate an agitation health parameter. In certain furtherembodiments, an example device further includes where the agitator isone of a rotating tank mixer, a large tank mixer, a portable tankmixers, a tote tank mixer, a drum mixer, a mounted mixer and a propellermixer.

An example system for estimating a vehicle steering system performanceparameter, includes: a data acquisition circuit structured to interpreta plurality of detection values, each of the plurality of detectionvalues corresponding to at least one of a plurality of input sensors,wherein the plurality of input sensors comprises at least one of anangular position sensor, an angular velocity sensor and an angularacceleration sensor positioned to measure the rotating component; a datastorage circuit structured to store specifications, system geometry, andanticipated state information for the vehicle steering system, the rack,the pinion, and the steering column, store historical steering systemperformance and buffer the plurality of detection values for apredetermined length of time; a bearing analysis circuit structured toanalyze buffered detection values relative to specifications andanticipated state information resulting in a bearing performanceparameter; and

a system analysis circuit structured to utilize the bearing performanceand at least one of an anticipated state, historical data and a systemgeometry to estimate a vehicle steering system performance parameter.

An example system for estimating a pump performance parameter, includes:a data acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for the pump and pump components, storehistorical steering system performance and buffer the plurality ofdetection values for a predetermined length of time; a bearing analysiscircuit structured to analyze buffered detection values relative tospecifications and anticipated state information resulting in a bearingperformance parameter; a system analysis circuit structured to utilizethe bearing performance and at least one of an anticipated state,historical data and a system geometry to estimate a pump performanceparameter. In certain embodiments, and example system further includeswherein the pump is a water pump in a car, and/or wherein the pump is amineral pump.

An example system for estimating a performance parameter for a drillingmachine, includes: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for the drilling machine and drillingmachine components, store historical drilling machine performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and

a system analysis circuit structured to utilize the bearing performanceand at least one of an anticipated state, historical data and a systemgeometry to estimate a performance parameter for the drilling machine.In certain further embodiments, the drilling machine is one of an oildrilling machine and a gas drilling machine.

An example system for estimating a performance parameter for a drillingmachine, includes: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for the drilling machine and drillingmachine components, store historical drilling machine performance andbuffer the plurality of detection values for a predetermined length oftime; a bearing analysis circuit structured to analyze buffereddetection values relative to specifications and anticipated stateinformation resulting in a bearing performance parameter; and a systemanalysis circuit structured to utilize bearing performance and at leastone of an anticipated state, historical data and a system geometry toestimate a performance parameter for the drilling machine.

Rotating components are used throughout many different types ofequipment and applications. Rotating components may include shafts,motors, rotors, stators, bearings, fins, vanes, wings, blades, fans,bearings, wheels, hubs, spokes, balls, rollers, pins, gears and thelike. In embodiments, information about the health or other status orstate information of or regarding a rotating component in a piece ofindustrial equipment or in an industrial process may be obtained bymonitoring the condition of the component or various other components ofthe industrial equipment or industrial process and identifying torsionon the component. Monitoring may include monitoring the amplitude andphase of a sensor signal, such as one measuring attributes such asangular position, angular velocity, angular acceleration, and the like.

An embodiment of a data monitoring device 9400 is shown in FIG. 61 andmay include a plurality of sensors 9406 communicatively coupled to acontroller 9402. The controller 9402 may include a data acquisitioncircuit 9404, a data storage circuit 9414, a system evaluation circuit9408 and, optionally, a response circuit 9410. The system evaluationcircuit 9408 may comprise a torsion analysis circuit 9412.

The plurality of sensors 9406 may be wired to ports on the dataacquisition circuit 9404. The plurality of sensors 9406 may bewirelessly connected to the data acquisition circuit 9404. The dataacquisition circuit 9404 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9406 where the sensors 9406 may be capturing data on differentoperational aspects of a bearing or piece of equipment orinfrastructure.

The selection of the plurality of sensors 9406 for a data monitoringdevice 9400 designed to assess torsion on a component, such as a shaft,motor, rotor, stator, bearing or gear, or other component describedherein, or a combination of components, such as within or comprising adrive train or piece of equipment or system, may depend on a variety ofconsiderations such as accessibility for installing new sensors,incorporation of sensors in the initial design, anticipated operationaland failure conditions, reliability of the sensors, and the like. Theimpact of failure may drive the extent to which a bearing or piece ofequipment is monitored with more sensors and/or higher capabilitysensors being dedicated to systems where unexpected or undetectedbearing failure would be costly or have severe consequences. To assesstorsion the sensors may include, among other options, an angularposition sensor and/or an angular velocity sensor and/or an angularacceleration sensor.

The system evaluation circuit 9408 may process the detection values toobtain information about one or more rotating components beingmonitored. The torsional analysis circuit 9412 may be structured toidentify torsion in a component or system, such as based on anticipatedstate, historical state, system geometry and the like, such as thatwhich is available from the data storage circuit 9414. The torsionalanalysis circuit 9412 may be structured to identify torsion using avariety of techniques such as amplitude, phase and frequency differencesin the detection values from two linear accelerometers positioned atdifferent locations on a shaft. The torsional analysis circuit 9412 mayidentify torsion using the difference in amplitude and phase between anangular accelerometer on a shaft and an angular accelerometer on a slipring on the end of the shaft. The torsional analysis circuit 9412 mayidentify shear stress/elongation on a component using two strain gaugesin a half bridge configuration or four strain gauges in a full bridgeconfiguration. The torsional analysis circuit 9412 may use coder basedtechniques such as markers to identify the rotation of a shaft, bearing,rotor, stator, gear or other rotating component. The markers beingassessed may include visual markers such as gear teeth or stripes on ashaft captured by an image sensor, light detector or the like. Themarkers being assessed may include magnetic components located on therotating component and sensed by an electromagnetic pickup. The sensormay be a Hall Effect sensor.

Additional input sensors may include a thermometer, a heat flux sensor,a magnetometer, an axial load sensor, a radial load sensor, anaccelerometer, a shear-stress torque sensor, a twist angle sensor andthe like. Twist angle may include rotational information at twopositions on shaft or an angular velocity or angular acceleration at twopositions on a shaft. In embodiments, the sensors may be positioned atdifferent ends of the shaft.

The torsional analysis circuit 9412 may include one or more of atransient signal analysis circuit and/or a frequency transformationcircuit and/or a frequency analysis circuit as described elsewhereherein.

In embodiments, the transitory signal analysis circuit for torsionalanalysis may include envelope modulation analysis, and other transitorysignal analysis techniques. The system evaluation circuit 9408 may storelong stream of detection values to the data storage circuit 9414. Thetransitory signal analysis circuit may use envelope analysis techniqueson those long streams of detection values to identify transient effects(such as impacts) which may not be identified by conventional sine waveanalysis (such as FFTs).

In embodiments, the frequencies of interest may include identifyingenergy at relation-order bandwidths for rotating equipment. The maximumorder observed may comprise a function of the bandwidth of the systemand the rotational speed of the component. For varying speeds (run-ups,run-downs, etc.), the minimum RPM may determine the maximum-observedorder. In embodiments, there may be torsional resonance at harmonics ofthe forcing frequency/frequency at which a component is being driven.

In an illustrative and non-limiting example, the monitoring device maybe used to collect and process sensor data to measure torsion on acomponent. The monitoring device may be in communication with or includea high resolution, high speed vibration sensor to collect data over anextended period of time, enough to measure multiple cycles of rotation.For gear driven equipment, the sampling resolution should be such thatthe number of samples taken per cycle is at least equal to the number ofgear teeth driving the component. It will be understood that a lowersampling resolution may also be utilized, which may result in a lowerconfidence determination and/or taking data over a longer period of timeto develop sufficient statistical confidence. This data may then be usedin the generation of a phase reference (relative probe) or tachometersignal for a piece of equipment. This phase reference may be used toalign phase data such as velocity and/or positional and/or accelerationdata from multiple sensors located at different positions on a componentor on different components within a system. This information mayfacilitate the determination of torsion for different components or thegeneration of an Operational Deflection Shape (“ODS”), indicating theextent of torsion on one or more components during an operational mode.

The higher resolution data stream may provide additional data for thedetection of transitory signals in low speed operations. Theidentification of transitory signals may enable the identification ofdefects in a piece of equipment or component.

In an illustrative and non-limiting example, the monitoring device maybe used to identify mechanical jitter for use in failure predictionmodels. The monitoring device may begin acquiring data when the piece ofequipment starts up through ramping up to operating speed or duringoperation. Once at operating speed, it is anticipated that the torsionaljitter should be minimal and changes in torsion during this phase may beindicative of cracks, bearing faults and the like. Additionally, knowntorsions may be removed from the signal to facilitate the identificationof unanticipated torsions resulting from system design flaws orcomponent wear. Having phase information associated with the datacollected at operating speed may facilitate identification of a locationof vibration and potential component wear. Relative phase informationfor a plurality of sensors located throughout a machine may facilitatethe evaluation of torsion as it is propagated through a piece ofequipment.

Based on the output of its various components, the system evaluationcircuit 9408 may make a component life prediction, identify a componenthealth parameter, identify a component performance parameter, and thelike. The system evaluation circuit 9408 may identify unexpected torsionon a rotating component, identify strain/stress of flexure bearings, andthe like. The system evaluation circuit 9408 may identify optimaloperation parameters for a piece of equipment to reduce torsion andextend component life. The system evaluation circuit 9408 may identifytorsion at selected operational frequencies (e.g., shaft rotationrates). Information about operational frequencies causing torsion mayfacilitate equipment operational balance in the future.

The system evaluation circuit 9408 may communicate with the data storagecircuit 9414 to access equipment specifications, equipment geometry,bearing specifications, component materials, anticipated stateinformation for a plurality of component types, operational history,historical detection values, and the like for use in assessing theoutput of its various components. The system evaluation circuit 9408 maybuffer a subset of the plurality of detection values, intermediate datasuch as time-based detection values, time-based detection valuestransformed to frequency information, filtered detection values,identified frequencies of interest, and the like for a predeterminedlength of time. The system evaluation circuit 9408 may periodicallystore certain detection values in the data storage circuit 9414 toenable the tracking of component performance over time. In embodiments,based on relevant operating conditions and/or failure modes, which mayoccur as detection values approach one or more criteria, the systemevaluation circuit 9408 may store data in the data storage circuit 9414based on the fit of data relative to one or more criteria, such as thosedescribed throughout this disclosure. Based on one sensor input meetingor approaching specified criteria or range, the system evaluationcircuit 9408 may store additional data such as RPM information,component loads, temperatures, pressures, vibrations or other sensordata of the types described throughout this disclosure in the datastorage circuit 9414. The system evaluation circuit 9408 may store datain the data storage circuit at a higher data rate for greatergranularity in future processing, the ability to reprocess at differentsampling rates, and/or to enable diagnosing or post-processing of systeminformation where operational data of interest is flagged, and the like.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors9406 may comprise, without limitation, one or more of the following: adisplacement sensor, an angular velocity sensor, an angularaccelerometer, a vibration sensor, an optical vibration sensor, athermometer, a hygrometer, a voltage sensor, a current sensor, anaccelerometer, a velocity detector, a light or electromagnetic sensor(e.g., determining temperature, composition and/or spectral analysis,and/or object position or movement), an image sensor, a structured lightsensor, a laser-based image sensor, an infrared sensor, an acoustic wavesensor, a heat flux sensor, a displacement sensor, a turbidity meter, aviscosity meter, a load sensor, a tri-axial vibration sensor, anaccelerometer, a tachometer, a fluid pressure meter, an air flow meter,a horsepower meter, a flow rate meter, a fluid particle detector, anacoustical sensor, a pH sensor, and the like, including, withoutlimitation, any of the sensors described throughout this disclosure andthe documents incorporated by reference.

The sensors 9406 may provide a stream of data over time that has a phasecomponent, such as relating to angular velocity, angular acceleration orvibration, allowing for the evaluation of phase or frequency analysis ofdifferent operational aspects of a piece of equipment or an operatingcomponent. The sensors 9406 may provide a stream of data that is notconventionally phase-based, such as temperature, humidity, load, and thelike. The sensors 9406 may provide a continuous or near continuousstream of data over time, periodic readings, event-driven readings,and/or readings according to a selected interval or schedule.

In an illustrative and non-limiting example, when assessing enginecomponents it may be desirable to remove vibrations due to the timing ofpiston vibrations or anticipated vibrational input due to crankshaftgeometry to assist in identifying other torsional forces on a component.This may assist in assessing the health of such diverse components as awater pump in a vehicle or positive displacement pumps.

In an illustrative and non-limiting example, torsional analysis and theidentification of variations in torsion may assist in the identificationof stick-slip in a gear or transfer system. In some cases, this may onlyoccur once per cycle, and phase information may be as important as ormore important than the amplitude of the signal in determining systemstate or behavior.

In an illustrative and non-limiting example, torsional analysis mayassist in the identification, prediction (e.g., timing) and evaluationof lash in a drive train and the follow-on torsion resulting from achange in direction or start up, which in turn may be used forcontrolling a system, assessing needs for maintenance, assessing needsfor balancing or otherwise re-setting components, or the like.

In an illustrative and non-limiting example, when assessing compressors,it may be desirable to remove vibrations due to the timing of pistonvibrations or anticipated vibrational input associated with thetechniques and geometry used for positive displacement compressors toassist in identifying other torsional forces on a component. This mayassist in assessing the health of compressors in such diverseenvironments as air conditioning units in factories, compressors in gashandling systems in an industrial environment, compressors in oilfields, and other environments as described elsewhere herein.

In an illustrative and non-limiting example, torsional analysis mayfacilitate the understanding of the health and expected life of variouscomponents associated with the drive trains of vehicles, such as cranes,bulldozers, tractors, haulers, backhoes, forklifts, agriculturalequipment, mining equipment, boring and drilling machines, diggingmachines, lifting machines, mixers (e.g., cement mixers), tank trucks,refrigeration trucks, security vehicles (e.g., including safes andsimilar facilities for preserving valuables), underwater vehicles,watercraft, aircraft, automobiles, trucks, trains and the like, as wellas drive trains of moving apparatus, such as assembly lines, lifts,cranes, conveyors, hauling systems, and others. The evaluation of thesensor data with the model of the system geometry and operatingconditions may be useful in identifying unexpected torsion and thetransmission of that torsion from the motor and drive shaft, from thedrive shaft to the universal joint and from the universal joint to oneor more wheel axles.

In an illustrative and non-limiting example, torsional analysis mayfacilitate in the understanding of the health and expected life ofvarious components associated with train/tram wheels and wheel sets. Asdiscussed above, torsional analysis may facilitate in the identificationof stick-slip between the wheels or wheel sets and the rail. Thetorsional analysis in view of the system geometry may facilitate theidentification of torsional vibration due to stick-slip as opposed tothe torsional vibration due to the driving geometry connecting theengine to the drive shaft to the wheel axle.

In embodiments, as illustrated in FIG. 61, the sensors 9406 may be partof the data monitoring device 9400, referred to herein in some cases asa data collector, which in some cases may comprise a mobile or portabledata collector. In embodiments, as illustrated in FIGS. 62 and 63, oneor more external sensors 9422, which are not explicitly part of amonitoring device 9416 but rather are new, previously attached to orintegrated into the equipment or component, may be opportunisticallyconnected to or accessed by the monitoring device 9416. The monitoringdevice 9416 may include a controller 9418. The controller 9418 mayinclude a data acquisition circuit 9420, a data storage circuit 9414, asystem evaluation circuit 9408 and, optionally, a response circuit 9410.The system evaluation circuit 9408 may comprise a torsional analysiscircuit 9412. The data acquisition circuit 9420 may include one or moreinput ports 9424. In embodiments as shown in FIG. 63, a data acquisitioncircuit 9420 may further comprise a wireless communications circuit9426. The one or more external sensors 9422 may be directly connected tothe one or more input ports 9424 on the data acquisition circuit 9420 ofthe controller 9418 or may be accessed by the data acquisition circuit9420 wirelessly using the wireless communications circuit 9426, such asby a reader, interrogator, or other wireless connection, such as over ashort-distance wireless protocol. The data acquisition circuit 9420 mayuse the wireless communications circuit 9426 to access detection valuescorresponding to the one or more external sensors 9422 wirelessly or viaa separate source or some combination of these methods.

In embodiments, as illustrated in FIG. 64, the data acquisition circuit9432 may further comprise a multiplexer circuit 9434 as describedelsewhere herein. Outputs from the multiplexer circuit 9434 may beutilized by the system evaluation circuit 9408. The response circuit9410 may have the ability to turn on or off portions of the multiplexorcircuit 9434. The response circuit 9410 may have the ability to controlthe control channels of the multiplexor circuit 9434.

The response circuit 9410 may initiate actions based on a componentperformance parameter, a component health value, a component lifeprediction parameter, and the like. The response circuit 9410 mayevaluate the results of the system evaluation circuit 9408 and, based oncertain criteria or the output from various components of the systemevaluation circuit 9408, may initiate an action. The criteria mayinclude identification of torsion on a component by the torsionalanalysis circuit. The criteria may include a sensor's detection valuesat certain frequencies or phases relative to a timer signal where thefrequencies or phases of interest may be based on the equipmentgeometry, equipment control schemes, system input, historical data,current operating conditions, and/or an anticipated response. Thecriteria may include a sensor's detection values at certain frequenciesor phases relative to detection values of a second sensor. The criteriamay include signal strength at certain resonant frequencies/harmonicsrelative to detection values associated with a system tachometer oranticipated based on equipment geometry and operation conditions.Criteria may include a predetermined peak value for a detection valuefrom a specific sensor, a cumulative value of a sensor's correspondingdetection value over time, a change in peak value, a rate of change in apeak value, and/or an accumulated value (e.g., a time spent above/belowa threshold value, a weighted time spent above/below one or morethreshold values, and/or an area of the detected value above/below oneor more threshold values). The criteria may comprise combinations ofdata from different sensors such as relative values, relative changes invalue, relative rates of change in value, relative values over time, andthe like. The relative criteria may change with other data orinformation such as process stage, type of product being processed, typeof equipment, ambient temperature and humidity, external vibrations fromother equipment, and the like. The relative criteria may be reflected inone or more calculated statistics or metrics (including ones generatedby further calculations on multiple criteria or statistics), which inturn may be used for processing (such as on board a data collector or byan external system), such as to be provided as an input to one or moreof the machine learning capabilities described in this disclosure, to acontrol system (which may be on board a data collector or remote, suchas to control selection of data inputs, multiplexing of sensor data,storage, or the like), or as a data element that is an input to anothersystem, such as a data stream or data package that may be available to adata marketplace, a SCADA system, a remote control system, a maintenancesystem, an analytic system, or other system.

Certain embodiments are described herein as detected values exceedingthresholds or predetermined values, but detected values may also fallbelow thresholds or predetermined values—for example where an amount ofchange in the detected value is expected to occur, but detected valuesindicate that the change may not have occurred. Except where the contextclearly indicates otherwise, any description herein describing adetermination of a value above a threshold and/or exceeding apredetermined or expected value is understood to include determinationof a value below a threshold and/or falling below a predetermined orexpected value.

The predetermined acceptable range may be based on anticipated torsionbased on equipment geometry, the geometry of a transfer system, anequipment configuration or control scheme, such as a piston firingsequence, and the like. The predetermined acceptable range may also bebased on historical performance or predicted performance, such as longterm analysis of signals and performance both from the past run and fromthe past several runs. The predetermined acceptable range may also bebased on historical performance or predicted performance, or based onlong term analysis of signals and performance across a plurality ofsimilar equipment and components (both within a specific environment,within an individual company, within multiple companies in the sameindustry and across industries). The predetermined acceptable range mayalso be based on a correlation of sensor data with actual equipment andcomponent performance.

In some embodiments, an alert may be issued based on some of thecriteria discussed above. In embodiments, the relative criteria for analarm may change with other data or information, such as process stage,type of product being processed on equipment, ambient temperature andhumidity, external vibrations from other equipment and the like. In anillustrative and non-limiting example, the response circuit 9410 mayinitiate an alert if a torsion in a component across a plurality ofcomponents exceeds a predetermined maximum value, if there is a changeor rate of change that exceeds a predetermined acceptable range, and/orif an accumulated value based on torsion amplitude and/or frequencyexceeds a threshold.

In embodiments, response circuit 9410 may cause the data acquisitioncircuit 9432 to enable or disable the processing of detection valuescorresponding to certain sensors based on some of the criteria discussedabove. This may include switching to sensors having different responserates, sensitivity, ranges, and the like; accessing new sensors or typesof sensors, and the like. Switching may be undertaken based on a model,a set of rules, or the like. In embodiments, switching may be undercontrol of a machine learning system, such that switching is controlledbased on one or more metrics of success, combined with input data, overa set of trials, which may occur under supervision of a human supervisoror under control of an automated system. Switching may involve switchingfrom one input port to another (such as to switch from one sensor toanother). Switching may involve altering the multiplexing of data, suchas combining different streams under different circumstances. Switchingmay involve activating a system to obtain additional data, such asmoving a mobile system (such as a robotic or drone system), to alocation where different or additional data is available (such aspositioning an image sensor for a different view or positioning a sonarsensor for a different direction of collection) or to a location wheredifferent sensors can be accessed (such as moving a collector to connectup to a sensor that is disposed at a location in an environment by awired or wireless connection). This switching may be implemented bychanging the control signals for a multiplexor circuit 9434 and/or byturning on or off certain input sections of the multiplexor circuit9434.

The response circuit 9410 may calculate transmission effectiveness basedon differences between a measured and theoretical angular position andvelocity of an output shaft after accounting for the gear ration and anyphase differential between input and output.

The response circuit 9410 may identify equipment or components that aredue for maintenance. The response circuit 9410 may make recommendationsfor the replacement of certain sensors in the future with sensors havingdifferent response rates, sensitivity, ranges, and the like. Theresponse circuit 9410 may recommend design alterations for futureembodiments of the component, the piece of equipment, the operatingconditions, the process, and the like.

In embodiments, the response circuit 9410 may recommend maintenance atan upcoming process stop or initiate a maintenance call. The responsecircuit 9410 may recommend changes in process or operating parameters toremotely balance the piece of equipment. In embodiments, the responsecircuit 9410 may implement or recommend process changes—for example tolower the utilization of a component that is near a maintenanceinterval, operating off-nominally, or failed for purpose but still atleast partially operational, to change the operating speed of acomponent (such as to put it in a lower-demand mode), to initiateamelioration of an issue (such as to signal for additional lubricationof a roller bearing set, or to signal for an alignment process for asystem that is out of balance), and the like.

In embodiments as shown in FIGS. 65, 66, 67, and 68, a data monitoringsystem 9460 may include at least one data monitoring device 9448. Atleast one data monitoring device 9448 may include sensors 9406 and acontroller 9438 comprising a data acquisition circuit 9404, a systemevaluation circuit 9408, a data storage circuit 9414, and acommunications circuit 9442. The system evaluation circuit 9408 mayinclude a torsional analysis circuit 9412. There may also be an optionalresponse circuit as described above and elsewhere herein. The systemevaluation circuit 9408 may periodically share data with thecommunication circuit 9442 for transmittal to the remote server 9440 toenable the tracking of component and equipment performance over time andunder varying conditions by a monitoring application 9446. Becauserelevant operating conditions and/or failure modes may occur as sensorvalues approach one or more criteria, the system evaluation circuit 9408may share data with the communication circuit 9462 for transmittal tothe remote server 9440 based on the fit of data relative to one or morecriteria. Based on one sensor input meeting or approaching specifiedcriteria or range, the system evaluation circuit 9408 may shareadditional data such as RPMs, component loads, temperatures, pressures,vibrations, and the like for transmittal. The system evaluation circuit9408 may share data at a higher data rate for transmittal to enablegreater granularity in processing on the remote server. In embodiments,as shown in FIG. 65, the communications circuit 9442 may communicatedata directly to a remote server 9440. In embodiments, as shown in FIG.66, the communications circuit 9442 may communicate data to anintermediate computer 9450 which may include a processor 9452 running anoperating system 9454 and a data storage circuit 9456.

In embodiments, as illustrated in FIGS. 67 and 68, a data collectionsystem 9458 may have a plurality of monitoring devices 9448 collectingdata on multiple components in a single piece of equipment, collectingdata on the same component across a plurality of pieces of equipment(both the same and different types of equipment) in the same facility aswell as collecting data from monitoring devices in multiple facilities.A monitoring application 9446 on a remote server 9440 may receive andstore one or more of detection values, timing signals and data comingfrom a plurality of the various monitoring devices 9448. In embodiments,as shown in FIG. 67, the communications circuit 9442 may communicatedata directly to a remote server 9440. In embodiments, as shown in FIG.68, the communications circuit 9442 may communicate data to anintermediate computer 9450, which may include a processor 9452 runningan operating system 9454 and a data storage circuit 9456. There may bean individual intermediate computer 9450 associated with each monitoringdevice 9264 or an individual intermediate computer 9450 may beassociated with a plurality of monitoring devices 9448 where theintermediate computer 9450 may collect data from a plurality of datamonitoring devices and send the cumulative data to the remote server9440.

The monitoring application 9446 may select subsets of detection values,timing signals, data, product performance and the like to be jointlyanalyzed. Subsets for analysis may be selected based on component type,component materials, or a single type of equipment in which a componentis operating. Subsets for analysis may be selected or grouped based oncommon operating conditions or operational history such as size of load,operational condition (e.g., intermittent, continuous), operating speedor tachometer, common ambient environmental conditions such as humidity,temperature, air or fluid particulate, and the like. Subsets foranalysis may be selected based on common anticipated state information.Subsets for analysis may be selected based on the effects of othernearby equipment such as nearby machines rotating at similarfrequencies, nearby equipment producing electromagnetic fields, nearbyequipment producing heat, nearby equipment inducing movement orvibration, nearby equipment emitting vapors, chemicals or particulates,or other potentially interfering or intervening effects.

The monitoring application 9446 may analyze a selected subset. In anillustrative example, data from a single component may be analyzed overdifferent time periods such as one operating cycle, cycle to cyclecomparisons, trends over several operating cycles/time such as a month,a year, the life of the component or the like. Data from multiplecomponents of the same type may also be analyzed over different timeperiods. Trends in the data such as changes in frequency or amplitudemay be correlated with failure and maintenance records associated withthe same component or piece of equipment. Trends in the data such aschanging rates of change associated with start-up or different points inthe process may be identified. Additional data may be introduced intothe analysis such as output product quality, output quantity (such asper unit of time), indicated success or failure of a process, and thelike. Correlation of trends and values for different types of data maybe analyzed to identify those parameters whose short-term analysis mightprovide the best prediction regarding expected performance. The analysismay identify model improvements to the model for anticipated stateinformation, recommendations around sensors to be used, positioning ofsensors and the like. The analysis may identify additional data tocollect and store. The analysis may identify recommendations regardingneeded maintenance and repair and/or the scheduling of preventativemaintenance. The analysis may identify recommendations around purchasingreplacement components and the timing of the replacement of thecomponents. The analysis may identify recommendations regarding futuregeometry changes to reduce torsion on components. The analysis mayresult in warning regarding dangers of catastrophic failure conditions.This information may be transmitted back to the monitoring device toupdate types of data collected and analyzed locally or to influence thedesign of future monitoring devices.

In embodiments, the monitoring application 9446 may have access toequipment specifications, equipment geometry, component specifications,component materials, anticipated state information for a plurality ofcomponent types, operational history, historical detection values,component life models and the like for use analyzing the selected subsetusing rule-based or model-based analysis. In embodiments, the monitoringapplication 9446 may feed a neural net with the selected subset to learnto recognize various operating states, health states (e.g., lifetimepredictions) and fault states utilizing deep learning techniques. Inembodiments, a hybrid of the two techniques (model-based learning anddeep learning) may be used.

In an illustrative and non-limiting example, the health of the rotatingcomponents on conveyors and lifters in an assembly line may be monitoredusing the torsional analysis techniques, data monitoring devices anddata collection systems described herein.

In an illustrative and non-limiting example, the health the rotatingcomponents in water pumps on industrial vehicles may be monitored usingthe torsional analysis techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents in compressors in gas handling systems may be monitored usingthe data monitoring devices and data collection systems describedherein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in compressors situated in the gas and oil fields may bemonitored using the data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in factory air conditioning units may be evaluated using thetechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents in factory mineral pumps may be evaluated using thetechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of the rotatingcomponents such as shafts, bearings, and gears in drilling machines andscrew drivers situated in the oil and gas fields may be evaluated usingthe torsional analysis techniques, data monitoring devices and datacollection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as shafts, bearings, gears, and rotors of motorssituated in the oil and gas fields may be evaluated using the torsionalanalysis techniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as blades, screws and other components of pumps situatedin the oil and gas fields may be evaluated using the torsional analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as shafts, bearings, motors, rotors, stators, gears, andother components of vibrating conveyors situated in the oil and gasfields may be evaluated using the torsional analysis techniques, datamonitoring devices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of mixers situated in the oil and gas fields may beevaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of centrifuges situated in oil and gas refineries maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of refining tanks situated in oil and gas refineriesmay be evaluated using the torsional analysis techniques, datamonitoring devices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of rotating tank/mixer agitators to promote chemicalreactions deployed in chemical and pharmaceutical production lines maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of mechanical/rotating agitators to promote chemicalreactions deployed in chemical and pharmaceutical production lines maybe evaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of rotatingcomponents such as bearings, shafts, motors, rotors, stators, gears, andother components of propeller agitators to promote chemical reactionsdeployed in chemical and pharmaceutical production lines may beevaluated using the torsional analysis techniques, data monitoringdevices and data collection systems described herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle steering mechanisms may be evaluated using the torsionalanalysis techniques, data monitoring devices and data collection systemsdescribed herein.

In an illustrative and non-limiting example, the health of bearings andassociated shafts, motors, rotors, stators, gears, and other componentsof vehicle engines may be evaluated using the torsional analysistechniques, data monitoring devices and data collection systemsdescribed herein.

In embodiments, a monitoring device for estimating an anticipatedlifetime of a rotating component in an industrial machine may comprise adata acquisition circuit structured to interpret a plurality ofdetection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a data storagecircuit structured to store specifications, system geometry, andanticipated state information for a plurality of rotating components,store historical component performance and buffer the plurality ofdetection values for a predetermined length of time; and a torsionalanalysis circuit structured to utilize transitory signal analysis toanalyze the buffered detection values relative to the rotating componentspecifications and anticipated state information resulting in theidentification of torsional vibration; and a system analysis circuitstructured to utilize the identified torsional vibration and at leastone of an anticipated state, historical data and a system geometry toidentify an anticipated lifetime of the rotating component. Inembodiments, the monitoring device may further comprise a responsecircuit to perform at least one operation in response to the anticipatedlifetime of the rotating component, wherein the plurality of inputsensors includes at least two sensors selected from the group consistingof a temperature sensor, a load sensor, an optical vibration sensor, anacoustic wave sensor, a heat flux sensor, an infrared sensor, anaccelerometer, a tri-axial vibration sensor, a tachometer, and the like.At least one operation may comprise issuing at least one of an alert anda warning, storing additional data in the data storage circuit, orderinga replacement of the rotating component, scheduling replacement of therotating component, recommending alternatives to the rotating component,and the like.

In embodiments, a monitoring device for evaluating the health of arotating component in an industrial machine may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors, wherein the plurality ofinput sensors comprises at least one of an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the health ofthe rotating component. In embodiments, the monitoring device mayfurther comprise a response circuit to perform at least one operation inresponse to the health of the rotating component. The plurality of inputsensors may include at least two sensors selected from the groupconsisting of a temperature sensor, a load sensor, an optical vibrationsensor, an acoustic wave sensor, a heat flux sensor, an infrared sensor,an accelerometer, a tri-axial vibration sensor a tachometer, and thelike. The monitoring device may issue an alert and an alarm, such as theat least one operation storing additional data in the data storagecircuit, ordering a replacement of the rotating component, schedulingreplacement of the rotating component, recommending alternatives to therotating component, and the like.

In embodiments, a monitoring device for evaluating the operational stateof a rotating component in an industrial machine may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors, wherein the plurality ofinput sensors comprises at least one of an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the operationalstate of the rotating component. In embodiments, the operational statemay be a current or future operational state. A response circuit mayperform at least one operation in response to the operational state ofthe rotating component. The at least one operation may store additionaldata in the data storage circuit, order a replacement of the rotatingcomponent, schedule a replacement of the rotating component,recommending alternatives to the rotating component, and the like.

In embodiments, s monitoring device for evaluating the operational stateof a rotating component in an industrial machine may include a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors, wherein the plurality ofinput sensors comprises at least one of an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in the identification oftorsional vibration; and a system analysis circuit structured to utilizethe identified torsional vibration and at least one of an anticipatedstate, historical data and a system geometry to identify the operationalstate of the rotating component, wherein the data acquisition circuitcomprises a multiplexer circuit whereby alternative combinations of thedetection values may be selected based on at least one of user input, adetected state and a selected operating parameter for a machine. Theoperational state may be a current or future operational state. The atleast one operation may enable or disable one or more portions of themultiplexer circuit, or altering the multiplexer control lines. The dataacquisition circuit may include at least two multiplexer circuits andthe at least one operation comprises changing connections between the atleast two multiplexer circuits.

In embodiments, a system for evaluating an operational state a rotatingcomponent in a piece of equipment may comprise a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors, wherein the plurality of input sensorscomprises at least one of an angular position sensor, an angularvelocity sensor and an angular acceleration sensor positioned to measurethe rotating component; a data storage circuit structured to storespecifications, system geometry, and anticipated state information for aplurality of rotating components, store historical component performanceand buffer the plurality of detection values for a predetermined lengthof time; and a torsional analysis circuit structured to utilizetransitory signal analysis to analyze the buffered detection valuesrelative to the rotating component specifications and anticipated stateinformation resulting in identification of any torsional vibration; asystem analysis circuit structured to utilize the torsional vibrationand at least one of an anticipated state, historical data and a systemgeometry to identify the operational state of the rotating component;and a communication module enabled to communicate the operational stateof the rotating component, the torsional vibration and detection valuesto a remote server, wherein the detection values communicated are basedpartly on the operational state of the rotating component and thetorsional vibration; and a monitoring application on the remote serverstructured to receive, store and jointly analyze a subset of thedetection values from the monitoring devices. The analysis of the subsetof detection values may include transitory signal analysis to identifythe presence of high frequency torsional vibration. The monitoringapplication may be structured to subset detection values based on oneof: operational state, torsional vibration, type of the rotatingcomponent, operational conditions under which detection values weremeasured, and type or equipment. The analysis of the subset of detectionvalues may include feeding a neural net with the subset of detectionvalues and supplemental information to learn to recognize variousoperating states, health states and fault states utilizing deep learningtechniques. The supplemental information may include one of componentspecification, component performance, equipment specification, equipmentperformance, maintenance records, repair records an anticipated statemodel, and the like. The operational state may include a current orfuture operational state. The monitoring device may include a responsecircuit to perform at least one operation in response to the operationalstate of the rotating component. The at least one operation may includestoring additional data in the data storage circuit.

In embodiments, a system for evaluating the health of a rotatingcomponent in a piece of equipment may comprise a data acquisitioncircuit structured to interpret a plurality of detection values, each ofthe plurality of detection values corresponding to at least one of aplurality of input sensors, wherein the plurality of input sensorscomprises at least one of: an angular position sensor, an angularvelocity sensor and an angular acceleration sensor positioned to measurethe rotating component; a data storage circuit structured to storespecifications, system geometry, and anticipated state information for aplurality of rotating components, store historical component performanceand buffer the plurality of detection values for a predetermined lengthof time; and a torsional analysis circuit structured to utilizetransitory signal analysis to analyze the buffered detection valuesrelative to the rotating component specifications and anticipated stateinformation resulting in identification of torsional vibration; a systemanalysis circuit structured to utilize the torsional vibration and atleast one of an anticipated state, historical data and a system geometryto identify the health of the rotating component; and a communicationmodule enabled to communicate the health of the rotating component, thetorsional vibrations and detection values to a remote server, whereinthe detection values communicated are based partly on the health of therotating component and the torsional vibration; and a monitoringapplication on the remote server structured to receive, store andjointly analyze a subset of the detection values from the monitoringdevices. In embodiments, the analysis of the subset of detection valuesmay include transitory signal analysis to identify the presence of highfrequency torsional vibration. The monitoring application may bestructured to subset detection values. The analysis of the subset ofdetection values may include feeding a neural net with the subset ofdetection values and supplemental information to learn to recognizevarious operating states, health states and fault states utilizing deeplearning techniques. The supplemental information may include one ofcomponent specification, component performance, equipment specification,equipment performance, maintenance records, repair records and ananticipated state model. The operational state may be a current orfuture operational state. A response circuit may perform at least oneoperation in response to the health of the rotating component.

In embodiments, a system for estimating an anticipated lifetime of arotating component in a piece of equipment may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors, wherein the plurality ofinput sensors comprises at least one of an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in identification of torsionalvibration; a system analysis circuit structured to utilize the torsionalvibration and at least one of an anticipated state, historical data anda system geometry to identify an anticipated life the rotatingcomponent; and a communication module enabled to communicate theanticipated life of the rotating component, the torsional vibrations anddetection values to a remote server, wherein the detection valuescommunicated are based partly on the anticipated life of the rotatingcomponent and the torsional vibration; and a monitoring application onthe remote server structured to receive, store and jointly analyze asubset of the detection values from the monitoring devices. Inembodiments, the analysis of the subset of detection values may includetransitory signal analysis to identify the presence of high frequencytorsional vibration. The monitoring application may be structured tosubset detection values based on one of anticipated life of the rotatingcomponent, torsional vibration, type of the rotating component,operational conditions under which detection values were measured, andtype of equipment. The analysis of the subset of detection values mayinclude feeding a neural net with the subset of detection values andsupplemental information to learn to recognize various operating states,health states, life expectancies and fault states utilizing deeplearning techniques. The supplemental information may include one ofcomponent specification, component performance, equipment specification,equipment performance, maintenance records, repair records and ananticipated state model. The monitoring device may include a responsecircuit to perform at least one operation in response to the anticipatedlife of the rotating component. The at least one operation may includeone of ordering a replacement of the rotating component, schedulingreplacement of the rotating component, and recommending alternatives tothe rotating component.

In embodiments, a system for evaluating the health of a variablefrequency motor in an industrial environment may comprise a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to atleast one of a plurality of input sensors, wherein the plurality ofinput sensors comprises at least one of an angular position sensor, anangular velocity sensor and an angular acceleration sensor positioned tomeasure the rotating component; a data storage circuit structured tostore specifications, system geometry, and anticipated state informationfor a plurality of rotating components, store historical componentperformance and buffer the plurality of detection values for apredetermined length of time; and a torsional analysis circuitstructured to utilize transitory signal analysis to analyze the buffereddetection values relative to the rotating component specifications andanticipated state information resulting in identification of torsionalvibration; a system analysis circuit structured to utilize the torsionalvibration and at least one of an anticipated state, historical data anda system geometry to identify a motor health parameter; and acommunication module enabled to communicate the motor health parameter,the torsional vibrations and detection values to a remote server,wherein the detection values communicated are based partly on the motorhealth parameter and the torsional vibration; and a monitoringapplication on the remote server structured to receive, store andjointly analyze a subset of the detection values from the monitoringdevices.

In embodiments, a system for data collection, processing, and torsionalanalysis of a rotating component in an industrial environment maycomprise a data acquisition circuit structured to interpret a pluralityof detection values, each of the plurality of detection valuescorresponding to at least one of a plurality of input sensors, whereinthe plurality of input sensors comprises at least one of an angularposition sensor, an angular velocity sensor and an angular accelerationsensor positioned to measure the rotating component; a streaming circuitfor streaming at least a subset of the acquired detection values to aremote learning system; and a remote learning system including atorsional analysis circuit structured to analyze the detection valuesrelative to a machine-based understanding of the state of the at leastone rotating component. The machine-based understanding may be developedbased on a model of the rotating component that determines a state ofthe at least one rotating component based at least in part on therelationship of the behavior of the rotating component to an operatingfrequency of a component of the industrial machine. The state of the atleast one rotating component may be at least one of an operating state,a health state, a predicted lifetime state and a fault state. Themachine-based understanding may be developed based by providing inputsto a deep learning machine, wherein the inputs comprise a plurality ofstreams of detection values for a plurality of rotating components and aplurality of measured state values for the plurality of rotatingcomponents. The state of the at least one rotating component may be atleast one of an operating state, a health state, a predicted lifetimestate and a fault state.

In embodiments, information about the health or other status or stateinformation of or regarding a component or piece of industrial equipmentmay be obtained by monitoring the condition of various componentsthroughout a process. Monitoring may include monitoring the amplitude ofa sensor signal measuring attributes such as temperature, humidity,acceleration, displacement and the like. An embodiment of a datamonitoring device 9700 is shown in FIG. 69 and may include a pluralityof sensors 9706 communicatively coupled to a controller 9702. Thecontroller 9702 may include a data acquisition circuit 9704, a signalevaluation circuit 9708, a data storage circuit 9716 and a responsecircuit 9710. The signal evaluation circuit 9708 may comprise a circuitfor detecting a fault in one or more sensors, or a set of sensors, suchas an overload detection circuit 9712, a sensor fault detection circuit9714, or both. Additionally, the signal evaluation circuit 9708 mayoptionally comprise one or more of a peak detection circuit, a phasedetection circuit, a bandpass filter circuit, a frequency transformationcircuit, a frequency analysis circuit, a phase lock loop circuit, atorsional analysis circuit, a bearing analysis circuit, and the like.

The plurality of sensors 9706 may be wired to ports on the dataacquisition circuit 9704. The plurality of sensors 9706 may bewirelessly connected to the data acquisition circuit 9704. The dataacquisition circuit 9704 may be able to access detection valuescorresponding to the output of at least one of the plurality of sensors9706 where the sensors 9706 may be capturing data on differentoperational aspects of a piece of equipment or an operating component.

The selection of the plurality of sensors 9706 for a data monitoringdevice 9700 designed for a specific component or piece of equipment maydepend on a variety of considerations such as accessibility forinstalling new sensors, incorporation of sensors in the initial design,anticipated operational and failure conditions, resolution desired atvarious positions in a process or plant, reliability of the sensors, andthe like. The impact of a failure, time response of a failure (e.g.,warning time and/or off-nominal modes occurring before failure),likelihood of failure, and/or sensitivity required and/or difficulty todetection failure conditions may drive the extent to which a componentor piece of equipment is monitored with more sensors and/or highercapability sensors being dedicated to systems where unexpected orundetected failure would be costly or have severe consequences.

Depending on the type of equipment, the component being measured, theenvironment in which the equipment is operating and the like, sensors9706 may comprise, without limitation, one or more of the following: avibration sensor, a thermometer, a hygrometer, a voltage sensor and/or acurrent sensor (for the component and/or other sensors measuring thecomponent), an accelerometer, a velocity detector, a light orelectromagnetic sensor (e.g., determining temperature, compositionand/or spectral analysis, and/or object position or movement), an imagesensor, a structured light sensor, a laser-based image sensor, a thermalimager, an acoustic wave sensor, a displacement sensor, a turbiditymeter, a viscosity meter, a axial load sensor, a radial load sensor, atri-axial sensor, an accelerometer, a speedometer, a tachometer, a fluidpressure meter, an air flow meter, a horsepower meter, a flow ratemeter, a fluid particle detector, an optical (laser) particle counter,an ultrasonic sensor, an acoustical sensor, a heat flux sensor, agalvanic sensor, a magnetometer, a pH sensor, and the like, including,without limitation, any of the sensors described throughout thisdisclosure and the documents incorporated by reference.

The sensors 9706 may provide a stream of data over time that has a phasecomponent, such as relating to acceleration or vibration, allowing forthe evaluation of phase or frequency analysis of different operationalaspects of a piece of equipment or an operating component. The sensors9706 may provide a stream of data that is not conventionallyphase-based, such as temperature, humidity, load, and the like. Thesensors 9706 may provide a continuous or near continuous stream of dataover time, periodic readings, event-driven readings, and/or readingsaccording to a selected interval or schedule.

Methods and systems are disclosed herein for a system for datacollection in an industrial environment using intelligent management ofdata collection bands, referred to herein in some cases as smart bands.Smart bands may facilitate intelligent, situational, context-awarecollection of data, such as by a data collector (such as any of the widerange of data collector embodiments described throughout thisdisclosure). Intelligent management of data collection via smart bandsmay improve various parameters of data collection, as well as parametersof the processes, applications, and products that depend on datacollection, such as data quality parameters, consistency parameters,efficiency parameters, comprehensiveness parameters, reliabilityparameters, effectiveness parameters, storage utilization parameters,yield parameters (including financial yield, output yield, and reductionof adverse events), energy consumption parameters, bandwidth utilizationparameters, input/output speed parameters, redundancy parameters,security parameters, safety parameters, interference parameters,signal-to-noise parameters, statistical relevancy parameters, andothers. Intelligent management of smart bands may optimize across one ormore such parameters, such as based on a weighting of the value of theparameters; for example, a smart band may be managed to provide a givenlevel of redundancy for critical data, while not exceeding a specifiedlevel of energy usage. This may include using a variety of optimizationtechniques described throughout this disclosure and the documentsincorporated herein by reference.

In embodiments, such methods and systems for intelligent management ofsmart bands include an expert system and supporting technologycomponents, services, processes, modules, applications and interfaces,for managing the smart bands (collectively referred to in some cases asa smart band platform 10722), which may include a model-based expertsystem, a rule-based expert system, an expert system using artificialintelligence (such as a machine learning system, which may include aneural net expert system, a self-organizing map system, ahuman-supervised machine learning system, a state determination system,a classification system, or other artificial intelligence system), orvarious hybrids or combinations of any of the above. References to anexpert system should be understood to encompass utilization of any oneof the foregoing or suitable combinations, except where contextindicates otherwise. Intelligent management may be of data collection ofvarious types of data (e.g., vibration data, noise data and other sensordata of the types described throughout this disclosure) for eventdetection, state detection, and the like. Intelligent management mayinclude managing a plurality of smart bands each directed at supportingan identified application, process or workflow, such as confirmingprogress toward or alignment with one or more objectives, goals, rules,policies, or guidelines. Intelligent management may also involvemanaging data collection bands targeted to backing out an unknownvariable based on collection of other data (such as based on a model ofthe behavior of a system that involves the variable), selectingpreferred inputs among available inputs (including specifyingcombinations, fusions, or multiplexing of inputs), and/or specifying aninput band among available input bands.

Data collection bands, or smart bands, may include any number of itemssuch as sensors, input channels, data locations, data streams, dataprotocols, data extraction techniques, data transformation techniques,data loading techniques, data types, frequency of sampling, placement ofsensors, static data points, metadata, fusion of data, multiplexing ofdata, and the like as described herein. Smart band settings, which maybe used interchangeably with smart band and data collection band, maydescribe the configuration and makeup of the smart band, such as byspecifying the parameters that define the smart band. For example, datacollection bands, or smart bands, may include one or more frequencies tomeasure. Frequency data may further include at least one of a group ofspectral peaks, a true-peak level, a crest factor derived from a timewaveform, and an overall waveform derived from a vibration envelope, aswell as other signal characteristics described throughout thisdisclosure. Smart bands may include sensors measuring or data regardingone or more wavelengths, one or more spectra, and/or one or more typesof data from various sensors and metadata. Smart bands may include oneor more sensors or types of sensors of a wide range of types, such asdescribed throughout this disclosure and the documents incorporated byreference herein. Indeed, the sensors described herein may be used inany of the methods or systems described throughout this disclosure. Forexample, one sensor may be an accelerometer, such as one that measuresvoltage per G (“V/G”) of acceleration (e.g., 100 mV/G, 500 mV/G, 1 V/G,5 V/G, 10 V/G, and the like). In embodiments, the data collection bandcircuit may alter the makeup of the subset of the plurality of sensorsused in a smart band based on optimizing the responsiveness of thesensor, such as for example choosing an accelerometer better suited formeasuring acceleration of a low speed mixer versus one better suited formeasuring acceleration of a high speed industrial centrifuge. Choosingmay be done intelligently, such as for example with a proximity probeand multiple accelerometers disposed on a centrifuge where while at lowspeed, one accelerometer is used for measuring in the smart band andanother is used at high speeds. Accelerometers come in various types,such as piezo-electric crystal, low frequency (e.g., 10 V/G), high speedcompressors (10 MV/G), MEMS, and the like. In another example, onesensor may be a proximity probe which can be used for sleeve or tilt-padbearings (e.g., oil bath), or a velocity probe. In yet another example,one sensor may be a solid-state relay (SSR) that is structured toautomatically interface with a routed data collector (such as a mobileor portable data collector) to obtain or deliver data. In anotherexample, a mobile or portable data collector may be routed to alter themakeup of the plurality of available sensors, such as by bringing anappropriate accelerometer to a point of sensing, such as on or near acomponent of a machine. In still another example, one sensor may be atriax probe (e.g., a 100 MV/G triax probe), that in embodiments is usedfor portable data collection. In some embodiments, of a triax probe, avertical element on one axis of the probe may have a high frequencyresponse while the ones mounted horizontally may influence the frequencyresponse of the whole triax. In another example, one sensor may be atemperature sensor and may include a probe with a temperature sensorbuilt inside, such as to obtain a bearing temperature. In stilladditional examples, sensors may be ultrasonic, microphone, touch,capacitive, vibration, acoustic, pressure, strain gauges, thermographic(e.g., camera), imaging (e.g., camera, laser, IR, structured light), afield detector, an EMF meter to measure an AC electromagnetic field, agaussmeter, a motion detector, a chemical detector, a gas detector, aCBRNE detector, a vibration transducer, a magnetometer, positional,location-based, a velocity sensor, a displacement sensor, a tachometer,a flow sensor, a level sensor, a proximity sensor, a pH sensor, ahygrometer/moisture sensor, a densitometric sensor, an anemometer, aviscometer, or any analog industrial sensor and/or digital industrialsensor. In a further example, sensors may be directed at detecting ormeasuring ambient noise, such as a sound sensor or microphone, anultrasound sensor, an acoustic wave sensor, and an optical vibrationsensor (e.g., using a camera to see oscillations that produce noise). Instill another example, one sensor may be a motion detector.

Data collection bands, or smart bands, may be of or may be configured toencompass one or more frequencies, wavelengths, or spectra forparticular sensors, for particular groups of sensors, or for combinedsignals from multiple sensors (such as involving multiplexing or sensorfusion).

Data collection bands, or smart bands, may be of or may be configured toencompass one or more sensors or sensor data (including groups ofsensors and combined signals) from one or more pieces ofequipment/components, areas of an installation, disparate butinterconnected areas of an installation (e.g., a machine assembly lineand a boiler room used to power the line), or locations (e.g., abuilding in Cambridge and a building in Boston). Smart band settings,configurations, instructions, or specifications (collectively referredto herein using any one of those terms) may include where to place asensor, how frequently to sample a data point or points, the granularityat which a sample is taken (e.g., a number of sampling points perfraction of a second), which sensor of a set of redundant sensors tosample, an average sampling protocol for redundant sensors, and anyother aspect that would affect data acquisition.

Within the smart band platform 10722, an expert system, which maycomprise a neural net, a model-based system, a rule-based system, amachine learning data analysis circuit, and/or a hybrid of any of those,may begin iteration towards convergence on a smart band that isoptimized for a particular goal or outcome, such as predicting andmanaging performance, health, or other characteristics of a piece ofequipment, a component, or a system of equipment or components. Based oncontinuous or periodic analysis of sensor data, as patterns/trends areidentified, or outliers appear, or a group of sensor readings begin tochange, etc., the expert system may modify its data collection bandsintelligently. This may occur by triggering a rule that reflects a modelor understanding of system behavior (e.g., recognizing a shift inoperating mode that calls for different sensors as velocity of a shaftincreases) or it may occur under control of a neural net (either incombination with a rule-based approach or on its own), where inputs areprovided such that the neural net over time learns to select appropriatecollection modes based on feedback as to successful outcomes (e.g.,successful classification of the state of a system, successfulprediction, successful operation relative to a metric, or the like). Forexample, when a new pressure reactor is installed in a chemicalprocessing facility, data from the current data collection band may notaccurately predict the state or metric of operation of the system, thus,the machine learning data analysis circuit may begin to iterate todetermine if a new data collection band is better at predicting a state.Based on offset system data, such as from a library or other datastructure, certain sensors, frequency bands or other smart band membersmay be used in the smart band initially and data may be collected toassess performance. As the neural net iterates, other sensors/frequencybands may be accessed to determine their relative weight in identifyingperformance metrics. Over time, a new frequency band may be identified(or a new collection of sensors, a new set of configurations forsensors, or the like) as a better gauge of performance in the system andthe expert system may modify its data collection band based on thisiteration. For example, perhaps a slightly different or older associatedturbine agitator in a chemical reaction facility dampens one or morevibration frequencies while a different frequency is of higher amplitudeand present during optimal performance than what was seen in the offsetsystem. In this example, the smart band may be altered from what wassuggested by the corresponding offset system to capture the higheramplitude frequency that is present in the current system.

The expert system, in embodiments involving a neural net or othermachine learning system, may be seeded and may iterate, such as towardsconvergence on a smart band, based on feedback and operation parameters,such as described herein. Certain feedback may include utilizationmeasures, efficiency measures (e.g., power or energy utilization, use ofstorage, use of bandwidth, use of input/output use of perishablematerials, use of fuel, and/or financial efficiency), measures ofsuccess in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, and profit measures. Certain parameters may include: storageparameters (e.g., data storage, fuel storage, storage of inventory andthe like); network parameters (e.g., network bandwidth, input/outputspeeds, network utilization, network cost, network speed, networkavailability and the like); transmission parameters (e.g., quality oftransmission of data, speed of transmission of data, error rates intransmission, cost of transmission and the like); security parameters(e.g., number and/or type of exposure events; vulnerability to attack,data loss, data breach, access parameters, and the like); location andpositioning parameters (e.g., location of data collectors, location ofworkers, location of machines and equipment, location of inventoryunits, location of parts and materials, location of network accesspoints, location of ingress and egress points, location of landingpositions, location of sensor sets, location of network infrastructure,location of power sources and the like); input selection parameters,data combination parameters (e.g., for multiplexing, extraction,transformation, loading, and the like); power parameters; states (e.g.,operating modes, availability states, environmental states, fault modes,maintenance modes, anticipated states); events; and equipmentspecifications. With respect to states, operating modes may includemobility modes (direction, speed, acceleration, and the like), type ofmobility modes (e.g., rolling, flying, sliding, levitation, hovering,floating, and the like), performance modes (e.g., gears, rotationalspeeds, heat levels, assembly line speeds, voltage levels, frequencylevels, and the like), output modes, fuel conversion modes, resourceconsumption modes, and financial performance modes (e.g., yield,profitability, and the like). Availability states may refer toanticipating conditions that could cause machine to go offline orrequire backup. Environmental states may refer to ambient temperature,ambient humidity/moisture, ambient pressure, ambient wind/fluid flow,presence of pollution or contaminants, presence of interfering elements(e.g., electrical noise, vibration), power availability, and powerquality. Anticipated states may include: achieving or not achieving adesired goal, such as a specified/threshold output production rate, aspecified/threshold generation rate, an operational efficiency/failurerate, a financial efficiency/profit goal, a power efficiency/resourceutilization; an avoidance of a fault condition (e.g., overheating, slowperformance, excessive speed, excessive motion, excessivevibration/oscillation, excessive acceleration, expansion/contraction,electrical failure, running out of stored power/fuel, overpressure,excessive radiation/melt down, fire, freezing, failure of fluid flow(e.g., stuck valves, frozen fluids); mechanical failures (e.g., brokencomponent, worn component, faulty coupling, misalignment,asymmetries/deflection, damaged component (e.g., deflection, strain,stress, cracking], imbalances, collisions, jammed elements, and lost orslipping chain or belt); avoidance of a dangerous condition orcatastrophic failure; and availability (online status).

The expert system may comprise or be seeded with a model that predictsan outcome or state given a set of data (which may comprise inputs fromsensors, such as via a data collector, as well as other data, such asfrom system components, from external systems and from external datasources). For example, the model may be an operating model for anindustrial environment, machine, or workflow. In another example, themodel may be for anticipating states, for predicting fault andoptimizing maintenance, for self-organizing storage (e.g., on devices,in data pools and/or in the cloud), for optimizing data transport (suchas for optimizing network coding, network-condition-sensitive routing,and the like), for optimizing data marketplaces, and the like.

The iteration of the expert system may result in any number ofdownstream actions based on analysis of data from the smart band. In anembodiment, the expert system may determine that the system shouldeither keep or modify operational parameters, equipment or a weightingof a neural net model given a desired goal, such as aspecified/threshold output production rate, specified/thresholdgeneration rate, an operational efficiency/failure rate, a financialefficiency/profit goal, a power efficiency/resource utilization, anavoidance of a fault condition, an avoidance of a dangerous condition orcatastrophic failure, and the like. In embodiments, the adjustments maybe based on determining context of an industrial system, such asunderstanding a type of equipment, its purpose, its typical operatingmodes, the functional specifications for the equipment, the relationshipof the equipment to other features of the environment (including anyother systems that provide input to or take input from the equipment),the presence and role of operators (including humans and automatedcontrol systems), and ambient or environmental conditions. For example,in order to achieve a profit goal, a pipeline in a refinery may need tooperate for a certain amount of time a day and/or at a certain flowrate. The expert system may be seeded with a model for operation of thepipeline in a manner that results in a specified profit goal, such asindicating a given flow rate of material through the pipeline based onthe current market sale price for the material and the cost of gettingthe material into the pipeline. As it acquires data and iterates, themodel will predict whether the profit goal will be achieved given thecurrent data. Based on the results of the iteration of the expertsystem, a recommendation may be made (or a control instruction may beautomatically provided) to operate the pipeline at a higher flow rate,to keep it operational for longer or the like. Further, as the systemiterates, one or more additional sensors may be sampled in the model todetermine if their addition to the smart band would improve predicting astate. In another embodiment, the expert system may determine that thesystem should either keep or modify operational parameters, equipment ora weighting of a neural net or other model given a constraint ofoperation (e.g., meeting a required endpoint (e.g., delivery date,amount, cost, coordination with another system), operating with alimited resource (e.g., power, fuel, battery), storage (e.g., datastorage), bandwidth (e.g., local network, p2p, WAN, internet bandwidth,availability, or input/output capacity), authorization (e.g.,role-based)), a warranty limitation, a manufacturer's guideline, amaintenance guideline). For example, a constraint of operating a boilerin a refinery is that the aeration of the boiler feedwater needs to bereduced in the cycle; therefore, the boiler must coordinate with thedeaerator. In this example, the expert system is seeded with a model foroperation of the boiler in coordination with the de-aerator that resultsin a specified overall performance. As sensor data from the system isacquired, the expert system may determine that an aspect of one or bothof the boiler and aerator must be changed to continue to achieve thespecific overall performance. In a further embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anidentified choke point. In still another embodiment, the expert systemmay determine that the system should either keep or modify operationalparameters, equipment or a weighting of a neural net model given anoff-nominal operation. For example, a reciprocating compressor in arefinery that delivers gases at high pressure may be measured as havingan off-nominal operation by sensors that feed their data into an expertsystem (optionally including a neural net or other machine learningsystem). As the expert system iterates and receives the off-nominaldata, it may predict that the refinery will not achieve a specified goaland will recommend an action, such as taking the reciprocatingcompressor offline for maintenance. In another embodiment, the expertsystem may determine that the system should collect more/fewer datapoints from one or more sensors. For example, an anchor agitator in apharmaceutical processing plant may be programmed to agitate thecontents of a tank until a certain level of viscosity (e.g., as measuredin centipoise) is obtained. As the expert system collects datathroughout the run indicating an increase in viscosity, the expertsystem may recommend collecting additional data points to confirm apredicted state in the face of the increased strain on the plant systemsfrom the viscosity. In yet another embodiment, the expert system maydetermine that the system should change a data storage technique. Instill another example, the expert system may determine that the systemshould change a data presentation mode or manner. In a furtherembodiment, the expert system may determine that the system should applyone or more filters (low pass, high pass, band pass, etc.) to collecteddata. In yet a further embodiment, the expert system may determine thatthe system should collect data from a new smart band/new set of sensorsand/or begin measuring a new aspect that the neural net identifieditself. For example, various measurements may be made of paddle-typeagitator mixers operating in a pharmaceutical plant, such as mixingtimes, temperature, homogeneous substrate distribution, heat exchangewith internal structures and the tank wall or oxygen transfer rate,mechanical stress, forces and torques on agitator vessels and internalstructures, and the like. Various sensor data streams may be included ina smart band monitoring these various aspects of the paddle-typeagitator mixer, such as a flow meter, a thermometer, and others. As theexpert system iterates, perhaps having been seeded with minimal datafrom during the agitator's run, a new aspect of the operation may becomeapparent, such as the impact of pH on the state of the run. Thus, a newsmart band will be identified by the expert system that includes sensordata from a pH meter. In yet still a further embodiment, the expertsystem may determine that the system should discontinue collection ofdata from a smart band, one or more sensors, or the like. In anotherembodiment, the expert system may determine that the system shouldinitiate data collection from a new smart band, such as a new smart bandidentified by the neural net itself. In yet another embodiment, theexpert system may determine that the system should adjust theweights/biases of a model used by the expert system. In still anotherembodiment, the expert system may determine that the system shouldremove/re-task under-utilized equipment. For example, a plurality ofagitators working with a pump blasting liquid in a pharmaceuticalprocessing plant may be monitored during operation of the plant by theexpert system. Through iteration of the expert system seeded with datafrom a run of the plant with the agitators, the expert system maypredict that a state will be achieved even if one or more agitators aretaken out of service.

In embodiments, a monitoring system for data collection in an industrialenvironment may include a plurality of input sensors, such as any ofthose described herein, communicatively coupled to a data collectorhaving a controller. The monitoring system may include a data collectionband circuit structured to determine at least one subset of theplurality of sensors from which to process output data. The monitoringsystem may also include a machine learning data analysis circuitstructured to receive output data from the at least one subset of theplurality of sensors and learn received output data patterns indicativeof a state. In some embodiments, the data collection band circuit mayalter the at least one subset of the plurality of sensors, or an aspectthereof, based on one or more of the learned received output datapatterns and the state. In certain embodiments, the machine learningdata analysis circuit is seeded with a model that enables it to learndata patterns. The model may be a physical model, an operational model,a system model, and the like. In other embodiments, the machine learningdata analysis circuit is structured for deep learning wherein input datais fed to the circuit with no or minimal seeding and the machinelearning data analysis circuit learns based on output feedback. Forexample, a static mixer in a chemical processing plant producingpolymers may be used to facilitate the polymerization reaction. Thestatic mixer may employ turbulent or laminar flow in its operation.Minimal data, such as heat transfer, velocity of flow out of the mixer,Reynolds number or pressure drop, acquired during the operation of thestatic mixer may be fed into the expert system which may iterate towardsa prediction based on initial feedback (e.g., viscosity of the polymer,color of the polymer, reactivity of the polymer).

There may be a balance of multiple goals/guidelines in the management ofsmart bands by the expert system. For example, a repair and maintenanceorganization (RMO) may have operating parameters designed formaintenance of a storage tank in a refinery, while the owner of therefinery may have particular operating parameters for the storage tankthat are designed for meeting a production goal. These goals, in thisexample relating to a maintenance goal or a production output, may betracked by a different data collection bands. For example, maintenanceof a storage tank may be tracked by sensors including a vibrationtransducer and a strain gauge, while the production goal of a storagetank may be tracked by sensors including a temperature sensor and a flowmeter. The expert system may (optionally using a neural net, machinelearning system, deep learning system, or the like, which may occurunder supervision by one or more supervisors (human or automated))intelligently manage bands aligned with different goals and assignweights, parameter modifications, or recommendations based on a factor,such as a bias towards one goal or a compromise to allow betteralignment with all goals being tracked, for example. Compromises amongthe goals delivered to the expert system may be based on one or morehierarchies or rules (relating to the authority, role, criticality, orthe like) of the applicable goals. In embodiments, compromises amonggoals may be optimized using machine learning, such as a neural net,deep learning system, or other artificial intelligence system asdescribed throughout this disclosure. In one illustrative example, in achemical processing plant where a gas-powered agitator is operating, theexpert system may manage multiple smart bands, such as one directed todetecting the operational status of the gas-powered agitator, onedirected at identifying a probability of hitting a production goal, andone directed at determining if the operation of the gas-powered agitatoris meeting a fuel efficiency goal. Each of these smart bands may bepopulated with different sensors or data from different sensors (e.g., avibration transducer to indicate operational status, a flow meter toindicate production goal, and a fuel gauge to indicate a fuelefficiency) whose output data are indicative of an aspect of theparticular goal. Where a single sensor or a set of sensors is helpfulfor more than one goal, overlapping smart bands (having some sensors incommon and other sensors not in common) may take input from that sensoror set of sensors, as managed by the smart band platform 10722. If thereare constraints on data collection (such as due to power limitations,storage limitations, bandwidth limitations, input/output processingcapabilities, or the like), a rule may indicate that one goal (e.g., afuel utilization goal or a pollution reduction goal that is mandated bylaw or regulation) takes precedence, such that the data collection forthe smart bands associated with that goal are maintained as others arepaused or shut down. Management of prioritization of goals may behierarchical or may occur by machine learning. The expert system may beseeded with models, or may not be seeded at all, in iterating towards apredicted state (i.e., meeting the goal) given the current data it hasacquired. In this example, during operation of the gas-powered agitator,the plant owner may decide to bias the system towards fuel efficiency.All of the bands may still be monitored, but as the expert systemiterates and predicts that the system will not meet or is not meeting aparticular goal, and then offers recommended changes directed atincreasing the chance of meeting the goal, the plant owner may structurethe system with a bias towards fuel efficiency so that the recommendedchanges to parameters affecting fuel efficiency are made in favor ofmaking other recommended changes.

In embodiments, the expert system may continue iterating in adeep-learning fashion to arrive at a single smart band, after beingseeded with more than one smart band, that optimizes meeting more thanone goal. For example, there may be multiple goals tracked for a thermicheating system in a chemical processing or a food processing plant, suchas thermal efficiency and economic efficiency. Thermal efficiency forthe thermic heating system may be expressed by comparing BTUs put intothe system, which can be obtained by knowing the amount of and qualityof the fuel being used, and the BTUs out of the system, which iscalculated using the flow out of the system and the temperaturedifferential of materials in and out of the system. Economic efficiencyof the thermic heating system may be expressed as the ratio betweencosts to run the system (including fuel, labor, materials, and services)and energy output from the system for a period of time. Data used totrack thermal efficiency may include data from a flow meter, qualitydata point(s), and a thermometer, and data used to track economicefficiency may be an energy output from the system (e.g., kWh) and costsdata. These data may be used in smart bands by the expert system topredict states, however, the expert system may iterate toward a smartband that is optimized to predict states related to both thermal andeconomic efficiency. The new smart band may include data used previouslyin the individual smart bands but may also use new data from differentsensors or data sources. In embodiments, the expert system may be seededwith a plurality of smart bands and iterate to predict various states,but may also iterate towards reducing the number of smart bands neededto predict the same set of states.

Iteration of the expert system may be governed by rules, in someembodiments. For example, the expert system may be structured to collectdata for seeding at a pre-determined frequency. The expert system may bestructured to iterate at least a number of times, such as when a newcomponent/equipment/fuel source is added, when a sensor goes off-line,or as standard practice. For example, when a sensor measuring therotation of a stirrer in a food processing line goes off-line and theexpert system begins acquiring data from a new sensor measuring the samedata points, the expert system may be structured to iterate for a numberof times before the state is utilized in or allowed to affect anydownstream actions. The expert system may be structured to trainoff-line or train in situ/online. The expert system may be structured toinclude static and/or manually input data in its smart bands. Forexample, an expert system managing smart bands associated with a mixerin a food processing plant may be structured to iterate towardspredicting a duration of mixing before the food being processed achievesa particular viscosity, wherein the smart band includes data regardingthe speed of the mixer, temperature of its contents, viscometricmeasurements and the required endpoint for viscosity and temperature ofthe food. The expert system may be structured to include aminimum/maximum number of variables.

In embodiments, the expert system may be overruled. In embodiments, theexpert system may revert to prior band settings, such as in the eventthe expert system fails, such as if a neural network fails in a neuralnet expert system, if uncertainty is too high in a model-based system,if the system is unable to resolve conflicting rules in rule-basedsystem, or the system cannot converge on a solution in any of theforegoing. For example, sensor data on an irrigation system used by theexpert system in a smart band may indicate a massive leak in the field,but visual inspection, such as by a drone, indicates no such leak. Inthis event, the expert system will revert to an original smart band forseeding the expert system. In another example, one or more point sensorson an industrial pressure cooker indicates imminent failure in a seal,but the data collection band that the expert system converged to with aweighting towards a performance metric did not identify the failure. Inthis event, the smart band will revert to an original setting or aversion of the smart band that would have also identified the imminentfailure of the pressure cooker seal. In embodiments, the expert systemmay change smart band settings in the event that a new component isadded that makes the system closer to a different offset system. Forexample, a vacuum distillation unit is added to an oil & gas refinery todistill naphthalene, but the current smart band settings for the expertsystem are derived from a refinery that distills kerosene. In thisexample, a data structure with smart band settings for various offsetsystems may be searched for a system that is more closely matched to thecurrent system. When a new offset system is identified as more closelymatched, such as one that also distill naphthalene, the new smart bandsettings (e.g., which sensors to use, where to place them, howfrequently to sample, what static data points are needed, etc. asdescribed herein) are used to seed the expert system to iterate towardspredicting a state for the system. In embodiments, the expert system maychange smart band settings in the event that a new set of offset data isavailable from a third-party library. For example, a pharmaceuticalprocessing plant may have optimized a catalytic reactor to operate in ahighly efficient way and deposited the smart band settings in a datastructure. The data structure may be continuously scanned for new smartbands that better aid in monitoring catalytic reactions and thus, resultin optimizing the operation of the reactor.

In embodiments, the expert system may be used to uncover unknownvariables. For example, the expert system may iterate to identify amissing variable to be used for further iterations, such as furtherneural net iterations. For example, an under-utilized tank in a legacycondensate/make-up water system of a power station may have an unknowncapacity because it is inaccessible and no documentation exists on thetank. Various aspects of the tank may be measured by a swarm of sensorsto arrive at an estimated volume (e.g., flow into a downstream space,duration of a dye traced solution to work through the system), then thatvolume can be fed into the neural net as a new variable in the smartband.

In embodiments, the location of expert system node locations may be on amachine, on a data collector (or a group of them), in a networkinfrastructure (enterprise or other), or in the cloud. In embodiments,there may be distributed neurons across nodes (e.g., machine, datacollector, network, cloud).

Referring to FIG. 71, in an aspect, a monitoring system 10700 for datacollection in an industrial environment, comprising a plurality of inputsensors 10702 communicatively coupled to a data collector 10704 having acontroller 10706, a data collection band circuit 10708 structured todetermine at least one collection parameter for at least one of theplurality of sensors 10702 from which to process output data 10710, anda machine learning data analysis circuit 10712 structured to receiveoutput data 10710 from the at least one of the plurality of sensors10702 and learn received output data patterns 10718 indicative of astate. The data collection band circuit 10708 alters the at least onecollection parameter for the at least one of the plurality of sensors10702 based on one or more of the learned received output data patterns10718 and the state. The state may correspond to an outcome relating toa machine in the environment, an anticipated outcome relating to amachine in the environment, an outcome relating to a process in theenvironment, an anticipated outcome relating to a process in theenvironment, and the like. The collection parameter may be a bandwidthparameter, may be used to govern the multiplexing of a plurality of theinput sensors, may be a timing parameter, may relate to a frequencyrange, may relate to the granularity of collection of sensor data, is astorage parameter for the collected data. The machine learning dataanalysis circuit may be structured to learn received output datapatterns 10718 by being seeded with a model 10720, which may be aphysical model, an operational model, or a system model. The machinelearning data analysis circuit may be structured to learn receivedoutput data patterns 10718 based on the state. The data collection bandcircuit may alter the subset of the plurality of sensors when thelearned received output data pattern does not reliably predict thestate, which may include discontinuing collection of data from the atleast one subset.

The monitoring system 10700 may keep or modify operational parameters ofan item of equipment in the environment based on the determined state.The controller 10706 may adjust the weighting of the machine learningdata analysis circuit 10712 based on the learned received output datapatterns 10718 or the state. The controller 10706 may collect more/fewerdata points from one or more members of the at least one subset ofplurality of sensors 10702 based on the learned received output datapatterns 10718 or the state. The controller 10706 may change a datastorage technique for the output data 10710 based on the learnedreceived output data patterns 10718 or the state. The controller 10706may change a data presentation mode or manner based on the learnedreceived output data patterns 10718 or the state. The controller 10706may apply one or more filters to the output data 10710. The controller10706 may identify a new data collection band circuit 10708 based on oneor more of the learned received output data patterns 10718 and thestate. The controller 10706 may adjust the weights/biases of the machinelearning data analysis circuit 10712, such as in response to the learnedreceived output data patterns 10718, in response to the accuracy of theprediction of an anticipated state by the machine learning data analysiscircuit, in response to the accuracy of a classification of a state bythe machine learning data analysis circuit, and the like. The monitoringdevice 10700 may remove or re-task under-utilized equipment based on oneor more of the learned received output data patterns 10718 and thestate. The machine learning data analysis circuit 10712 may include aneural network expert system. At least one subset of the plurality ofsensors measures vibration and noise data. The machine learning dataanalysis circuit 10712 may be structured to learn received output datapatterns 10718 indicative of progress/alignment with one or moregoals/guidelines, wherein progress/alignment of each goal/guideline maybe determined by a different subset of the plurality of sensors. Themachine learning data analysis circuit 10712 may be structured to learnreceived output data patterns 10718 indicative of an unknown variable.The machine learning data analysis circuit 10712 may be structured tolearn received output data patterns 10718 indicative of a preferredinput among available inputs. The machine learning data analysis circuit10712 may be structured to learn received output data patterns 10718indicative of a preferred input data collection band among availableinput data collection bands. The machine learning data analysis circuit10712 may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof.

In embodiments, a monitoring device for data collection in an industrialenvironment may include a plurality of input sensors 10702communicatively coupled to a controller 10706, the controller 10706including a data collection band circuit 10708 structured to determineat least one subset of the plurality of sensors 10702 from which toprocess output data 10710; and a machine learning data analysis circuit10712 structured to receive output data from the at least one subset ofthe plurality of sensors 10702 and learn received output data patterns10718 indicative of a state, wherein the data collection band circuit10708 alters an aspect of the at least one subset of the plurality ofsensors 10702 based on one or more of the learned received output datapatterns 10718 and the state. The aspect that the data collection bandcircuit 10708 alters is a number or a frequency of data points collectedfrom one or more members of the at least one subset of plurality ofsensors 10702. The aspect that the data collection band circuit 10708alters is a bandwidth parameter, a timing parameter, a frequency range,a granularity of collection of sensor data, a storage parameter for thecollected data, and the like.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns indicative of a state, wherein the datacollection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the data collection band circuit 10708 alters theat least one of the plurality of sensors 10702 when the learned receivedoutput data pattern 10718 does not reliably predict the state.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns 10718 indicative of a state, wherein thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the data collector 10704 collects more or fewerdata points from the at least one of the plurality of sensors 10702based on the learned received output data patterns 10718 or the state.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data 10710 patterns indicative of a state, wherein thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the controller 10706 changes a data storagetechnique for the output data 10710 based on the learned received outputdata patterns 10718 or the state.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns 10718 indicative of a state, wherein thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the controller 10706 changes a data presentationmode or manner based on the learned received output data patterns 10718or the state.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns 10718 indicative of a state, wherein thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the controller 10706 identifies a new datacollection band circuit 10708 based on one or more of the learnedreceived output data patterns 10718 and the state.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712 structured to receive output data10710 from the at least one of the plurality of sensors 10702 and learnreceived output data patterns 10718 indicative of a state, wherein thedata collection band circuit 10708 alters the at least one collectionparameter for the at least one of the plurality of sensors 10702 basedon one or more of the learned received output data patterns 10718 andthe state, and wherein the controller 10706 adjusts the weights/biasesof the machine learning data analysis circuit 10712. The adjustment maybe in response to the learned received output data patterns, in responseto the accuracy of the prediction of an anticipated state by the machinelearning data analysis circuit, in response to the accuracy of aclassification of a state by the machine learning data analysis circuit,and the like.

In an embodiment, a monitoring system 10700 for data collection in anindustrial environment may include a plurality of input sensors 10702communicatively coupled to a data collector 10704 having a controller10706, a data collection band circuit 10708 structured to determine atleast one collection parameter for at least one of the plurality ofsensors 10702 from which to process output data 10710, and a machinelearning data analysis circuit 10712. This machine learning dataanalysis circuit is structured to receive output data 10710 from the atleast one of the plurality of sensors 10702 and learn received outputdata patterns 10718 indicative of a state, wherein the data collectionband circuit 10708 alters the at least one collection parameter for theat least one of the plurality of sensors 10702 based on one or more ofthe learned received output data patterns 10718 and the state, andwherein the machine learning data analysis circuit 10712 is structuredto learn received output data patterns 10718 indicative of progress oralignment with one or more goals or guidelines.

Clause 1. In embodiments, a monitoring system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a data collector having a controller; a datacollection band circuit structured to determine at least one collectionparameter for at least one of the plurality of sensors from which toprocess output data; and a machine learning data analysis circuitstructured to receive output data from the at least one of the pluralityof sensors and learn received output data patterns indicative of astate, wherein the data collection band circuit alters the at least onecollection parameter for the at least one of the plurality of sensorsbased on one or more of the learned received output data patterns andthe state. 2. The system of clause 1, wherein the state corresponds toan outcome relating to a machine in the environment. 3. The system ofclause 1, wherein the state corresponds to an anticipated outcomerelating to a machine in the environment. 4. The system of clause 1,wherein the state corresponds to an outcome relating to a process in theenvironment. 5. The system of clause 1, wherein the state corresponds toan anticipated outcome relating to a process in the environment. 6. Thesystem of clause 1, wherein the collection parameter is a bandwidthparameter. 7. The system of clause 1, wherein the collection parameteris used to govern the multiplexing of a plurality of the input sensors.8. The system of clause 1, wherein the collection parameter is a timingparameter. 9. The system of clause 1, wherein the collection parameterrelates to a frequency range. 10. The system of clause 1, wherein thecollection parameter relates to the granularity of collection of sensordata. 11. The system of clause 1, wherein the collection parameter is astorage parameter for the collected data. 12. The system of clause 1,wherein the machine learning data analysis circuit is structured tolearn received output data patterns by being seeded with a model. 13.The system of clause 12, wherein the model is a physical model, anoperational model, or a system model. 14. The system of clause 1,wherein the machine learning data analysis circuit is structured tolearn received output data patterns based on the state. 15. The systemof clause 1, wherein the data collection band circuit alters the subsetof the plurality of sensors when the learned received output datapattern does not reliably predict the state. 16. The system of clause15, wherein altering at least one subset comprises discontinuingcollection of data from the at least one subset. 17. The system ofclause 1, wherein the monitoring system keeps or modifies operationalparameters of an item of equipment in the environment based on thedetermined state. 18. The system of clause 1, wherein the controlleradjusts the weighting of the machine learning data analysis circuitbased on the learned received output data patterns or the state. 19. Thesystem of clause 1, wherein the controller collects more or fewer datapoints from one or more members of the at least one subset of pluralityof sensors based on the learned received output data patterns or thestate. 20. The system of clause 1, wherein the controller changes a datastorage technique for the output data based on the learned receivedoutput data patterns or the state. 21. The system of clause 1, whereinthe controller changes a data presentation mode or manner based on thelearned received output data patterns or the state. 22. The system ofclause 1, wherein the controller applies one or more filters to theoutput data. 23. The system of clause 1, wherein the controlleridentifies a new data collection band circuit based on one or more ofthe learned received output data patterns and the state. 24. The systemof clause 1, wherein the controller adjusts the weights/biases of themachine learning data analysis circuit. 25. The system of clause 24,wherein the adjustment is in response to the learned received outputdata patterns. 26. The system of clause 24, wherein the adjustment is inresponse to the accuracy of the prediction of an anticipated state bythe machine learning data analysis circuit. 27. The system of clause 24,wherein the adjustment is in response to the accuracy of aclassification of a state by the machine learning data analysis circuit.28. The system of clause 1, wherein the monitoring deviceremoves/re-tasks under-utilized equipment based on one or more of thelearned received output data patterns and the state. 29. The system ofclause 1, wherein the machine learning data analysis circuit comprises aneural network expert system. 30. The system of clause 1, wherein the atleast one subset of the plurality of sensors measures vibration andnoise data. 31. The system of clause 1, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns indicative of progress/alignment with one or moregoals/guidelines. 32. The system of clause 31, whereinprogress/alignment of each goal/guideline is determined by a differentsubset of the plurality of sensors. 33. The system of clause 1, whereinthe machine learning data analysis circuit is structured to learnreceived output data patterns indicative of an unknown variable. 34. Thesystem of clause 1, wherein the machine learning data analysis circuitis structured to learn received output data patterns indicative of apreferred input among available inputs. 35. The system of clause 1,wherein the machine learning data analysis circuit is structured tolearn received output data patterns indicative of a preferred input datacollection band among available input data collection bands. 36. Thesystem of clause 1, wherein the machine learning data analysis circuitis disposed in part on a machine, on one or more data collectors, innetwork infrastructure, in the cloud, or any combination thereof. 37. Amonitoring device for data collection in an industrial environment,comprising: a plurality of input sensors communicatively coupled to acontroller, the controller comprising: a data collection band circuitstructured to determine at least one subset of the plurality of sensorsfrom which to process output data; and a machine learning data analysiscircuit structured to receive output data from the at least one subsetof the plurality of sensors and learn received output data patternsindicative of a state, wherein the data collection band circuit altersan aspect of the at least one subset of the plurality of sensors basedon one or more of the learned received output data patterns and thestate. 38. The system of clause 37, wherein the aspect that the datacollection band circuit alters is a number of data points collected fromone or more members of the at least one subset of plurality of sensors.39. The system of clause 37, wherein the aspect that the data collectionband circuit alters is a frequency of data points collected from one ormore members of the at least one subset of plurality of sensors. 40. Thesystem of clause 37, wherein the aspect that the data collection bandcircuit alters is a bandwidth parameter. 41. The system of clause 37,wherein the aspect that the data collection band circuit alters is atiming parameter. 42. The system of clause 37, wherein the aspect thatthe data collection band circuit alters relates to a frequency range.43. The system of clause 37, wherein the aspect that the data collectionband circuit alters relates to the granularity of collection of sensordata. 44. The system of clause 37, wherein the collection parameter is astorage parameter for the collected data. 45. A monitoring system fordata collection in an industrial environment, comprising: a plurality ofinput sensors communicatively coupled to a data collector having acontroller; a data collection band circuit structured to determine atleast one collection parameter for at least one of the plurality ofsensors from which to process output data; and a machine learning dataanalysis circuit structured to receive output data from the at least oneof the plurality of sensors and learn received output data patternsindicative of a state, wherein the data collection band circuit altersthe at least one collection parameter for the at least one of theplurality of sensors based on one or more of the learned received outputdata patterns and the state, and wherein the data collection bandcircuit alters the at least one of the plurality of sensors when thelearned received output data pattern does not reliably predict thestate. 46. A monitoring system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a data collector having a controller; a data collection bandcircuit structured to determine at least one collection parameter for atleast one of the plurality of sensors from which to process output data;and a machine learning data analysis circuit structured to receiveoutput data from the at least one of the plurality of sensors and learnreceived output data patterns indicative of a state, wherein the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state, and wherein thedata collector collects more or fewer data points from the at least oneof the plurality of sensors based on the learned received output datapatterns or the state. 47. A monitoring system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a data collector having a controller; a datacollection band circuit structured to determine at least one collectionparameter for at least one of the plurality of sensors from which toprocess output data; and a machine learning data analysis circuitstructured to receive output data from the at least one of the pluralityof sensors and learn received output data patterns indicative of astate, wherein the data collection band circuit alters the at least onecollection parameter for the at least one of the plurality of sensorsbased on one or more of the learned received output data patterns andthe state, and wherein the controller changes a data storage techniquefor the output data based on the learned received output data patternsor the state. 48. A monitoring system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a data collector having a controller; a datacollection band circuit structured to determine at least one collectionparameter for at least one of the plurality of sensors from which toprocess output data; and a machine learning data analysis circuitstructured to receive output data from the at least one of the pluralityof sensors and learn received output data patterns indicative of astate, wherein the data collection band circuit alters the at least onecollection parameter for the at least one of the plurality of sensorsbased on one or more of the learned received output data patterns andthe state, and wherein the controller changes a data presentation modeor manner based on the learned received output data patterns or thestate. 49. A monitoring system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a data collector having a controller; a data collection bandcircuit structured to determine at least one collection parameter for atleast one of the plurality of sensors from which to process output data;and a machine learning data analysis circuit structured to receiveoutput data from the at least one of the plurality of sensors and learnreceived output data patterns indicative of a state, wherein the datacollection band circuit alters the at least one collection parameter forthe at least one of the plurality of sensors based on one or more of thelearned received output data patterns and the state, and wherein thecontroller identifies a new data collection band circuit based on one ormore of the learned received output data patterns and the state. 50. Amonitoring system for data collection in an industrial environment,comprising: a plurality of input sensors communicatively coupled to adata collector having a controller; a data collection band circuitstructured to determine at least one collection parameter for at leastone of the plurality of sensors from which to process output data; and amachine learning data analysis circuit structured to receive output datafrom the at least one of the plurality of sensors and learn receivedoutput data patterns indicative of a state, wherein the data collectionband circuit alters the at least one collection parameter for the atleast one of the plurality of sensors based on one or more of thelearned received output data patterns and the state, and wherein thecontroller adjusts the weights/biases of the machine learning dataanalysis circuit. 51. The system of clause 50, wherein the adjustment isin response to the learned received output data patterns. 52. The systemof clause 50, wherein the adjustment is in response to the accuracy ofthe prediction of an anticipated state by the machine learning dataanalysis circuit. 53. The system of clause 50, wherein the adjustment isin response to the accuracy of a classification of a state by themachine learning data analysis circuit. 54. A monitoring system for datacollection in an industrial environment, comprising: a plurality ofinput sensors communicatively coupled to a data collector having acontroller; a data collection band circuit structured to determine atleast one collection parameter for at least one of the plurality ofsensors from which to process output data; and a machine learning dataanalysis circuit structured to receive output data from the at least oneof the plurality of sensors and learn received output data patternsindicative of a state, wherein the data collection band circuit altersthe at least one collection parameter for the at least one of theplurality of sensors based on one or more of the learned received outputdata patterns and the state, and wherein the machine learning dataanalysis circuit is structured to learn received output data patternsindicative of progress or alignment with one or more goals orguidelines.

As described elsewhere herein, an expert system in an industrialenvironment may use sensor data to make predictions about outcomes orstates of the environment or items in the environment. Data collectionmay be of various types of data (e.g., vibration data, noise data andother sensor data of the types described throughout this disclosure) forevent detection, state detection, and the like. For example, the expertsystem may utilize ambient noise, or the overall sound environment ofthe area and/or overall vibration of the device of interest, optionallyin conjunction with other sensor data, in detecting or predicting eventsor states. For example, a reciprocating compressor in a refinery, whichmay generate its own vibration, may also have an ambient vibrationthrough contact with other aspects of the system.

In embodiments, all three types of noise (ambient noise, local noise andvibration noise) including various subsets thereof and combinations withother types of data, may be organized into large data sets, along withmeasured results, that are processed by a “deep learning” machine/expertsystem that learns to predict one or more states (e.g., maintenance,failure, or operational) or overall outcomes, such as by learning fromhuman supervision or from other feedback, such as feedback from one ormore of the systems described throughout this disclosure and thedocuments incorporated by reference herein.

Throughout this disclosure, various examples will involve machines,components, equipment, assemblies, and the like, and it should beunderstood that the disclosure could apply to any of the aforementioned.Elements of these machines operating in an industrial environment (e.g.,rotating elements, reciprocating elements, swinging elements, flexingelements, flowing elements, suspending elements, floating elements,bouncing elements, bearing elements, etc.) may generate vibrations thatmay be of a specific frequency and/or amplitude typical of the elementwhen the element is in a given operating condition or state (e.g., anormal mode of operation of a machine at a given speed, in a given gear,or the like). Changes in a parameter of the vibration may be indicativeor predictive of a state or outcome of the machine. Various sensors maybe useful in measuring vibration, such as accelerometers, velocitytransducers, imaging sensors, acoustic sensors, and displacement probes,which may collectively be known as vibration sensors. Vibration sensorsmay be mounted to the machine, such as permanently or temporarily (e.g.,adhesive, hook-and-loop, or magnetic attachment), or may be disposed ona mobile or portable data collector. Sensed conditions may be comparedto historical data to identify or predict a state, condition or outcome.Typical faults that can be identified using vibration analysis include:machine out of balance, machine out of alignment, resonance, bentshafts, gear mesh disturbances, blade pass disturbances, vane passdisturbances, recirculation & cavitation, motor faults (rotor & stator),bearing failures, mechanical looseness, critical machine speeds, and thelike, as well as excessive friction, clutch slipping, belt problems,suspension and shock absorption problems, valve and other fluid leaks,under-pressure states in lubrication and other fluid systems,overheating (such as due to many of the above), blockage or freezing ofengagement of mechanical systems, interference effects, and other faultsdescribed throughout this disclosure and in the documents incorporatedby reference.

Given that machines are frequently found adjacent to or working inconcert with other machinery, measuring the vibration of the machine maybe complicated by the presence of various noise components in theenvironment or associated vibrations that the machine may be subjectedto. Indeed, the ambient and/or local environment may have its ownvibration and/or noise pattern that may be known. In embodiments, thecombination of vibration data with ambient and/or local noise or otherambient sensed conditions may form its own pattern, as will be furtherdescribed herein.

In embodiments, measuring vibration noise may involve one or morevibration sensors on or in a machine to measure vibration noise of themachine that occurs continuously or periodically. Analysis of thevibration noise may be performed, such as filtering, signalconditioning, spectral analysis, trend analysis, and the like. Analysismay be performed on aggregate or individual sensor measurements toisolate vibration noise of equipment to obtain a characteristicvibration, vibration pattern or “vibration fingerprint” of the machine.The vibration fingerprints may be stored in a data structure, orlibrary, of vibration fingerprints. The vibration fingerprints mayinclude frequencies, spectra (i.e., frequency vs. amplitude),velocities, peak locations, wave peak shapes, waveform shapes, waveenvelope shapes, accelerations, phase information, phase shifts(including complex phase measurements) and the like. Vibrationfingerprints may be stored in the library in association with aparameter by which it may be searched or sorted. The parameters mayinclude a brand or type of machine/component/equipment, location ofsensor(s) attachment or placement, duty cycle of the equipment/machine,load sharing of the equipment/machine, dynamic interactions with otherdevices, RPM, flow rate, pressure, other vibration drivingcharacteristic, voltage of line power, age of equipment, time ofoperation, known neighboring equipment, associated auxiliaryequipment/components, size of space equipment is in, material ofplatform for equipment, heat flux, magnetic fields, electrical fields,currents, voltage, capacitance, inductance, aspect of a product, andcombinations (e.g., simple ratios) of the same. Vibration fingerprintsmay be obtained for machines under normal operation or for other periodsof operation (e.g., off-nominal operation, malfunction, maintenanceneeded, faulty component, incorrect parameters of operation, otherconditions, etc.) and can be stored in the library for comparison tocurrent data. The library of vibration fingerprints may be stored asindicators with associated predictions, states, outcomes and/or events.Trend analysis data of measured vibration fingerprints can indicate timebetween maintenance events/failure events.

In embodiments, vibration noise may be used by the expert system toconfirm the status of a machine, such as a favorable operation, aproduction rate, a generation rate, an operational efficiency, afinancial efficiency (e.g., output per cost), a power efficiency, andthe like. In embodiments, the expert system may make a comparison of thevibration noise with a stored vibration fingerprint. In otherembodiments, the expert system may be seeded with vibration noise andinitial feedback on states and outcomes in order to learn to predictother states and outcomes. For example, a center pivot irrigation systemmay be remotely monitored by attached vibration sensors to provide ameasured vibration noise that can be compared to a library of vibrationfingerprints to confirm that the system is operating normally. If thesystem is not operating normally, the expert system may automaticallydispatch a field crew or drone to investigate. In another example of avacuum distillation unit in a refinery, the vibration noise may becompared, such as by the expert system, to stored vibration fingerprintsin a library to confirm a production rate of diesel. In a furtherexample, the expert system may be seeded with vibration noise for apipeline under conditions of a normal production rate and as the expertsystem iterates with current data (e.g., altered vibration noise, andpossibly other altered parameters), it may predict that the productionrate has increased as caused by the alterations. Measurements may becontinually analyzed in this way to remotely monitor operation.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict when maintenance is required (e.g., off-nominalmeasurement, artifacts in signal, etc.), such as when vibration noise ismatched to a condition when the equipment/component requiredmaintenance, vibration noise exceeds a threshold/limit, vibration noiseexceeds a threshold/limit or matches a library vibration fingerprinttogether with one or more additional parameters, as described herein.For example, when the vibration fingerprint from a turbine agitator in apharmaceutical processing plant matches a vibration fingerprint for aturbine agitator when it required a replacement bearing, the expertsystem may cause an action to occur, such as immediately shutting downthe agitator or scheduling its shutdown and maintenance.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict a failure or an imminent failure. For example,vibration noise from a gas agitator in a pharmaceutical processing plantmay be matched to a condition when the agitator previously failed or wasabout to fail. In this example, the expert system may immediately shutdown the agitator, schedule its shutdown, or cause a backup agitator tocome online. In another example, vibration noise from a pump blastingliquid agitator in a chemical processing plant may exceed a threshold orlimit and the expert system may cause an investigation into the cause ofthe excess vibration noise, shut down the agitator, or the like. Inanother example, vibration noise from an anchor agitator in apharmaceutical processing plant may exceed a threshold/limit or match alibrary vibration fingerprint together with one or more additionalparameters (see parameters herein), such as a decreased flow rate,increased temperature, or the like. Using vibration noise taken togetherwith the parameters, the expert system may more reliably predict thefailure or imminent failure.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to predict or diagnose a problem (e.g., unbalanced, misaligned,worn, or damaged) with the equipment or an external source contributingvibration noise to the equipment. For example, when the vibration noisefrom a paddle-type agitator mixer matches a vibration fingerprint from aprior imbalance, the expert system may immediately shut down the mixer.

In embodiments, when the expert system makes a prediction of an outcomeor state using vibration noise, the expert system may perform adownstream action, or cause it to be performed. Downstream actions mayinclude: triggering an alert of a failure, imminent failure, ormaintenance event; shutting down equipment/component; initiatingmaintenance/lubrication/alignment; deploying a field technician;recommending a vibration absorption/dampening device; modifying aprocess to utilize backup equipment/component; modifying a process topreserve products/reactants, etc.; generating/modifying a maintenanceschedule; coupling the vibration fingerprint with duty cycle of theequipment, RPM, flow rate, pressure, temperature or othervibration-driving characteristic to obtain equipment/component statusand generate a report, and the like. For example, vibration noise for acatalytic reactor in a chemical processing plant may be matched to acondition when the catalytic reactor required maintenance. Based on thispredicted state of required maintenance, the expert system may deploy afield technician to perform the maintenance.

In embodiments, the library may be updated if a changed parameterresulted in a new vibration fingerprint, or if a predicted outcome orstate did not occur in the absence of mitigation. In embodiments, thelibrary may be updated if a vibration fingerprint was associated with analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the vibrationfingerprint, or the standard deviation for the match in order to acceptthe predicted outcome.

In embodiments, vibration noise may be compared, such as by the expertsystem, to stored vibration fingerprints and associated states andoutcomes in the library, or alternatively, may be used to seed an expertsystem to determine if a change in a system parameter external orinternal to the machine has an effect on its intrinsic operation. Inembodiments, a change in one or more of a temperature, flow rate,materials in use, duration of use, power source, installation, or otherparameter (see parameters above) may alter the vibration fingerprint ofa machine. For example, in a pressure reactor in a chemical processingplant, the flow rate and a reactant may be changed. The changes mayalter the vibration fingerprint of the machine such that the vibrationfingerprint stored in the library for normal operation is no longercorrect.

Ambient noise, or the overall sound environment of the area and/oroverall vibration of the device of interest, optionally in conjunctionwith other ambient sensed conditions, may be used in detecting orpredicting events, outcomes, or states. Ambient noise may be measured bya microphone, ultrasound sensors, acoustic wave sensors, opticalvibration sensors (e.g., using a camera to see oscillations that producenoise), or “deep learning” neural networks involving various sensorarrays that learn, using large data sets, to identify patterns, soundstypes, noise types, etc. In an embodiment, the ambient sensed conditionmay relate to motion detection. For example, the motion may be aplatform motion (e.g., vehicle, oil platform, suspended platform onland, etc.) or an object motion (e.g., moving equipment, people, robots,parts (e.g., fan blades or turbine blades), etc.). In an embodiment, theambient sensed condition may be sensed by imaging, such as to detect alocation and nature of various machines, equipment, and other objects,such as ones that might impact local vibration. In an embodiment, theambient sensed condition may be sensed by thermal detection and imaging(e.g., for presence of people; presence of heat sources that may affectperformance parameters, etc.). In an embodiment, the ambient sensedcondition may be sensed by field detection (e.g., electrical, magnetic,etc.). In an embodiment, the ambient sensed condition may be sensed bychemical detection (e.g., smoke, other conditions). Any sensor data maybe used by the expert system to provide an ambient sensed condition foranalysis along with the vibration fingerprint to predict an outcome,event, or state. For example, an ambient sensed condition near a stirreror mixer in a food processing plant may be the operation of a spaceheater during winter months, wherein the ambient sensed condition mayinclude an ambient noise and an ambient temperature.

In an aspect, local noise may be the noise or vibration environmentwhich is ambient, but known to be locally generated. The expert systemmay filter out ambient noise, employ common mode noise removal, and/orphysically isolate the sensing environment.

In embodiments, a system for data collection in an industrialenvironment may use ambient, local and vibration noise for prediction ofoutcomes, events, and states. A library may be populated with each ofthe three noise types for various conditions (e.g., start up, shut down,normal operation, other periods of operation as described elsewhereherein). In other embodiments, the library may be populated with noisepatterns representing the aggregate ambient, local, and/or vibrationnoise. Analysis (e.g., filtering, signal conditioning, spectralanalysis, trend analysis) may be performed on the aggregate noise toobtain a characteristic noise pattern and identify changes in noisepattern as possible indicators of a changed condition. A library ofnoise patterns may be generated with established vibration fingerprintsand local and ambient noise that can be sorted by a parameter (seeparameters herein), or other parameters/features of the local andambient environment (e.g., company type, industry type, products,robotic handling unit present/not present, operating environment, flowrates, production rates, brand or type of auxiliary equipment (e.g.,filters, seals, coupled machinery)). The library of noise patterns maybe used by an expert system, such as one with machine learning capacity,to confirm a status of a machine, predict when maintenance is required(e.g., off-nominal measurement, artifacts in signal), predict a failureor an imminent failure, predict/diagnose a problem, and the like.

Based on a current noise pattern, the library may be consulted or usedto seed an expert system to predict an outcome, event, or state based onthe noise pattern. Based on the prediction, the expert system may one ormore of trigger an alert of a failure, imminent failure, or maintenanceevent, shut down equipment/component/line, initiatemaintenance/lubrication/alignment, deploy a field technician, recommenda vibration absorption/dampening device, modify a process to utilizebackup equipment/component, modify a process to preserveproducts/reactants, etc., generate/modify a maintenance schedule, or thelike.

For example, a noise pattern for a thermic heating system in apharmaceutical plant or cooking system may include local, ambient, andvibration noise. The ambient noise may be a result of, for example,various pumps to pump fuel into the system. Local noise may be a resultof a local security camera chirping with every detection of motion.Vibration noise may result from the combustion machinery used to heatthe thermal fluid. These noise sources may form a noise pattern whichmay be associated with a state of the thermic system. The noise patternand associated state may be stored in a library. An expert system usedto monitor the state of the thermic heating system may be seeded withnoise patterns and associated states from the library. As current dataare received into the expert system, it may predict a state based onhaving learned noise patterns and associated states.

In another example, a noise pattern for boiler feed water in a refinerymay include local and ambient noise. The local noise may be attributedto the operation of, for example, a feed pump feeding the feed waterinto a steam drum. The ambient noise may be attributed to nearby fans.These noise sources may form a noise pattern which may be associatedwith a state of the boiler feed water. The noise pattern and associatedstate may be stored in a library. An expert system used to monitor thestate of the boiler may be seeded with noise patterns and associatedstates from the library. As current data are received into the expertsystem, it may predict a state based on having learned noise patternsand associated states.

In yet another example, a noise pattern for a storage tank in a refinerymay include local, ambient, and vibration noise. The ambient noise maybe a result of, for example, a pump that pumps a product into the tank.Local noise may be a result of a fan ventilating the tank room.Vibration noise may result from line noise of a power supply into thestorage tank. These noise sources may form a noise pattern which may beassociated with a state of the storage tank. The noise pattern andassociated state may be stored in a library. An expert system used tomonitor the state of the storage tank may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

In another example, a noise pattern for condensate/make-up water systemin a power station may include vibration and ambient noise. The ambientnoise may be attributed to nearby fans. The vibration noise may beattributed to the operation of the condenser. These noise sources mayform a noise pattern which may be associated with a state of thecondensate/make-up water system. The noise pattern and associated statemay be stored in a library. An expert system used to monitor the stateof the condensate/make-up water system may be seeded with noise patternsand associated states from the library. As current data are receivedinto the expert system, it may predict a state based on having learnednoise patterns and associated states.

A library of noise patterns may be updated if a changed parameterresulted in a new noise pattern or if a predicted outcome or state didnot occur in the absence of mitigation of a diagnosed problem. A libraryof noise patterns may be updated if a noise pattern resulted in analternative state than what was predicted by the library. The update mayoccur after just one time that the state that actually occurred did notmatch the predicted state from the library. In other embodiments, it mayoccur after a threshold number of times. In embodiments, the library maybe updated to apply one or more rules for comparison, such as rules thatgovern how many parameters to match along with the noise pattern, or thestandard deviation for the match in order to accept the predictedoutcome. For example, a baffle may be replaced in a static agitator in apharmaceutical processing plant which may result in a changed noisepattern. In another example, as the seal on a pressure cooker in a foodprocessing plant ages, the noise pattern associated with the pressurecooker may change.

In embodiments, the library of vibration fingerprints, noise sourcesand/or noise patterns may be available for subscription. The librariesmay be used in offset systems to improve operation of the local system.Subscribers may subscribe at any level (e.g., component, machinery,installation, etc.) in order to access data that would normally not beavailable to them, such as because it is from a competitor, or is froman installation of the machinery in a different industry not typicallyconsidered. Subscribers may search on indicators/predictors based on orfiltered by system conditions, or update an indicator/predictor withproprietary data to customize the library. The library may furtherinclude parameters and metadata auto-generated by deployed sensorsthroughout an installation, onboard diagnostic systems andinstrumentation and sensors, ambient sensors in the environment, sensors(e.g., in flexible sets) that can be put into place temporarily, such asin one or more mobile data collectors, sensors that can be put intoplace for longer term use, such as being attached to points of intereston devices or systems, and the like.

In embodiments, a third party (e.g., RMOs, manufacturers) can aggregatedata at the component level, equipment level, factory/installation leveland provide a statistically valid data set against which to optimizetheir own systems. For example, when a new installation of a machine iscontemplated, it may be beneficial to review a library for best datapoints to acquire in making state predictions. For example, a particularsensor package may be recommended to reliably determine if there will bea failure. For example, if vibration noise of equipment coupled withparticular levels of local noise or other ambient sensed conditionsreliably is an indicator of imminent failure, a given vibrationtransducer/temp/microphone package observing those elements may berecommended for the installation. Knowing such information may informthe choice to rent or buy a piece of machinery or associated warrantiesand service plans, such as based on knowing the quantity and depth ofinformation that may be needed to reliably maintain the machinery.

In embodiments, manufacturers may utilize the library to rapidly collectin-service information for machines to draft engineering specificationsfor new customers.

In embodiments, noise and vibration data may be used to remotely monitorinstalls and automatically dispatch a field crew.

In embodiments, noise and vibration data may be used to audit a system.For example, equipment running outside the range of a licensed dutycycle may be detected by a suite of vibration sensors and/orambient/local noise sensors. In embodiments, alerts may be triggered ofpotential out-of-warranty violations based on data from vibrationsensors and/or ambient/local noise sensors.

In embodiments, noise and vibration data may be used in maintenance.This may be particularly useful where multiple machines are deployedthat may vibrationally interact with the environment, such as two largegenerating machines on the same floor or platform with each other, suchas in power generation plants.

In embodiments (FIG. 72), a monitoring system 10800 for data collectionin an industrial environment, may include a plurality of sensors 10802selected among vibration sensors, ambient environment condition sensorsand local sensors for collecting non-vibration data proximal to amachine in the environment, the plurality of sensors 10802communicatively coupled to a data collector 10804, a data collectioncircuit 10808 structured to collect output data 10810 from the pluralityof sensors 10802, and a machine learning data analysis circuit 10812structured to receive the output data 10810 and learn received outputdata patterns 10814 predictive of at least one of an outcome and astate. The state may correspond to an outcome relating to a machine inthe environment, an anticipated outcome relating to a machine in theenvironment, an outcome relating to a process in the environment, or ananticipated outcome relating to a process in the environment. The systemmay be deployed on the data collector 10804 or distributed between thedata collector 10804 and a remote infrastructure. The data collector10804 may include the data collection circuit 10808. The ambientenvironment condition or local sensors include one or more of a noisesensor, a temperature sensor, a flow sensor, a pressure sensor, achemical sensor, a vibration sensor, an acceleration sensor, anaccelerometer, a Pressure sensor, a force sensor, a position sensor, alocation sensor, a velocity sensor, a displacement sensor, a temperaturesensor, a thermographic sensor, a heat flux sensor, a tachometer sensor,a motion sensor, a magnetic field sensor, an electrical field sensor, agalvanic sensor, a current sensor, a flow sensor, a gaseous flow sensor,a non-gaseous fluid flow sensor, a heat flow sensor, a particulate flowsensor, a level sensor, a proximity sensor, a toxic gas sensor, achemical sensor, a CBRNE sensor, a pH sensor, a hygrometer, a moisturesensor, a densitometer, an imaging sensor, a camera, an SSR, a triaxprobe, an ultrasonic sensor, a touch sensor, a microphone, a capacitivesensor, a strain gauge, an EMF meter, and the like.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to a data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein themonitoring system 10800 is structured to determine if the output datamatches a learned received output data pattern. The machine learningdata analysis circuit 10812 may be structured to learn received outputdata patterns 10814 by being seeded with a model 10816. The model 10816may be a physical model, an operational model, or a system model. Themachine learning data analysis circuit 10812 may be structured to learnreceived output data patterns 10814 based on the outcome or the state.The monitoring system 10700 keeps or modifies operational parameters orequipment based on the predicted outcome or the state. The datacollection circuit 10808 collects more or fewer data points from one ormore of the plurality of sensors 10802 based on the learned receivedoutput data patterns 10814, the outcome or the state. The datacollection circuit 10808 changes a data storage technique for the outputdata based on the learned received output data patterns 10814, theoutcome, or the state. The data collector 10804 changes a datapresentation mode or manner based on the learned received output datapatterns 10814, the outcome, or the state. The data collection circuit10808 applies one or more filters (low pass, high pass, band pass, etc.)to the output data. The data collection circuit 10808 adjusts theweights/biases of the machine learning data analysis circuit 10812, suchas in response to the learned received output data patterns 10814. Themonitoring system 10800 removes/re-tasks under-utilized equipment basedon one or more of the learned received output data patterns 10814, theoutcome, or the state. The machine learning data analysis circuit 10812may include a neural network expert system. The machine learning dataanalysis circuit 10812 may be structured to learn received output datapatterns 10814 indicative of progress/alignment with one or moregoals/guidelines, wherein progress/alignment of each goal/guideline isdetermined by a different subset of the plurality of sensors 10802. Themachine learning data analysis circuit 10812 may be structured to learnreceived output data patterns 10814 indicative of an unknown variable.The machine learning data analysis circuit 10812 may be structured tolearn received output data patterns 10814 indicative of a preferredinput sensor among available input sensors. The machine learning dataanalysis circuit 10812 may be disposed in part on a machine, on one ormore data collection circuits 10808, in network infrastructure, in thecloud, or any combination thereof. The output data 10810 from thevibration sensors forms a vibration fingerprint, which may include oneor more of a frequency, a spectrum, a velocity, a peak location, a wavepeak shape, a waveform shape, a wave envelope shape, an acceleration, aphase information, and a phase shift. The data collection circuit 10808may apply a rule regarding how many parameters of the vibrationfingerprint to match or the standard deviation for the match in order toidentify a match between the output data 10810 and the learned receivedoutput data pattern. The state may be one of a normal operation, amaintenance required, a failure, or an imminent failure. The monitoringsystem 10800 may trigger an alert, shut down equipment/component/line,initiate maintenance/lubrication/alignment based on the predictedoutcome or state, deploy a field technician based on the predictedoutcome or state, recommend a vibration absorption/dampening devicebased on the predicted outcome or state, modify a process to utilizebackup equipment/component based on the predicted outcome or state, andthe like. The monitoring system 10800 may modify a process to preserveproducts/reactants, etc. based on the predicted outcome or state. Themonitoring system 10800 may generate or modify a maintenance schedulebased on the predicted outcome or state. The data collection circuit10808 may include the data collection circuit 10808. The system may bedeployed on the data collection circuit 10808 or distributed between thedata collection circuit 10808 and a remote infrastructure.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein themonitoring system 10800 is structured to determine if the output datamatches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from the plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, and amachine learning data analysis circuit 10812 structured to receive theoutput data 10810 and learn received output data patterns 10814predictive of at least one of an outcome and a state, wherein the outputdata 10810 from the vibration sensors forms a vibration fingerprint. Thevibration fingerprint may include one or more of a frequency, aspectrum, a velocity, a peak location, a wave peak shape, a waveformshape, a wave envelope shape, an acceleration, a phase information, anda phase shift. The data collection circuit 10808 may apply a ruleregarding how many parameters of the vibration fingerprint to match orthe standard deviation for the match in order to identify a matchbetween the output data 10810 and the learned received output datapattern. The monitoring system 10800 may be structured to determine ifthe output data matches a learned received output data pattern and keepor modify operational parameters or equipment based on thedetermination.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection band circuit 10818that identifies a subset of the plurality of sensors 10802 from which toprocess output data, the sensors selected among vibration sensors,ambient environment condition sensors and local sensors for collectingnon-vibration data proximal to a machine in the environment, theplurality of sensors 10802 communicatively coupled to a data collectionband circuit 10818, a data collection circuit 10808 structured tocollect the output data 10810 from the subset of plurality of sensors10802, and a machine learning data analysis circuit 10812 structured toreceive the output data 10810 and learn received output data patterns10814 predictive of at least one of an outcome and a state, wherein whenthe learned received output data patterns 10814 do not reliably predictthe outcome or the state, the data collection band circuit 10818 altersat least one parameter of at least one of the plurality of sensors10802. A controller 10806 identifies a new data collection band circuit10818 based on one or more of the learned received output data patterns10814 and the outcome or state. The machine learning data analysiscircuit 10812 may be further structured to learn received output datapatterns 10814 indicative of a preferred input data collection bandamong available input data collection bands. The system may be deployedon the data collection circuit 10808 or distributed between the datacollection circuit 10808 and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may include a data collection circuit 10808 structured tocollect output data 10810 from a plurality of sensors 10802, the sensorsselected among vibration sensors, ambient environment condition sensorsand local sensors for collecting non-vibration data proximal to amachine in the environment, the plurality of sensors 10802communicatively coupled to the data collection circuit 10808, whereinthe output data 10810 from the vibration sensors is in the form of avibration fingerprint, a data structure 10820 comprising a plurality ofvibration fingerprints and associated outcomes, and a machine learningdata analysis circuit 10812 structured to receive the output data 10810and learn received output data patterns 10814 predictive of an outcomeor a state based on processing of the vibration fingerprints. Themachine learning data analysis circuit 10812 may be seeded with one ofthe plurality of vibration fingerprints from the data structure 10820.The data structure 10820 may be updated if a changed parameter resultedin a new vibration fingerprint or if a predicted outcome did not occurin the absence of mitigation. The data structure 10820 may be updatedwhen the learned received output data patterns 10814 do not reliablypredict the outcome or the state. The system may be deployed on the datacollection circuit or distributed between the data collection circuitand a remote infrastructure.

In embodiments, a monitoring system 10800 for data collection in anindustrial environment may include a data collection circuit 10808structured to collect output data 10810 from a plurality of sensors10802 selected among vibration sensors, ambient environment conditionsensors and local sensors for collecting non-vibration data proximal toa machine in the environment, the plurality of sensors 10802communicatively coupled to a data collection circuit 10808, wherein theoutput data 10810 from the plurality of sensors 10802 is in the form ofa noise pattern, a data structure 10820 comprising a plurality of noisepatterns and associated outcomes, and a machine learning data analysiscircuit 10812 structured to receive the output data 10810 and learnreceived output data patterns 10814 predictive of an outcome or a statebased on processing of the noise patterns.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a plurality of sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment, the plurality of sensors communicatively coupled to a datacollector; a data collection circuit structured to collect output datafrom the plurality of sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns predictive of at least one of an outcome and a state. Thestate may correspond to an outcome, anticipated outcome, outcomerelating to a process, as relating to a machine in the environment. Thesystem may be deployed on the data collector. The system may bedistributed between the data collector and a remote infrastructure. Theambient environment condition sensors may include a noise sensor, atemperature sensor, a flow sensor, a pressure sensor, include a chemicalsensor, a noise sensor, a temperature sensor, a flow sensor, a pressuresensor, a chemical sensor, a vibration sensor, an acceleration sensor,an accelerometer, a pressure sensor, a force sensor, a position sensor,a location sensor, a velocity sensor, a displacement sensor, atemperature sensor, a thermographic sensor, a heat flux sensor, atachometer sensor, a motion sensor, a magnetic field sensor, anelectrical field sensor, a galvanic sensor, a current sensor, a flowsensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, a heatflow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter. The local sensors may comprise one or more of a vibration sensor,an acceleration sensor, an accelerometer, a pressure sensor, a forcesensor, a position sensor, a location sensor, a velocity sensor, adisplacement sensor, a temperature sensor, a thermographic sensor, aheat flux sensor, a tachometer sensor, a motion sensor, a magnetic fieldsensor, an electrical field sensor, a galvanic sensor, a current sensor,a flow sensor, a gaseous flow sensor, a non-gaseous fluid flow sensor, aheat flow sensor, a particulate flow sensor, a level sensor, a proximitysensor, a toxic gas sensor, a chemical sensor, a CBRNE sensor, a pHsensor, a hygrometer, a moisture sensor, a densitometer, an imagingsensor, a camera, an SSR, a triax probe, an ultrasonic sensor, a touchsensor, a microphone, a capacitive sensor, a strain gauge, and an EMFmeter.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein themonitoring system is structured to determine if the output data matchesa learned received output data pattern. In embodiments, the machinelearning data analysis circuit may be structured to learn receivedoutput data patterns by being seeded with a model, such as where themodel is a physical model, an operational model, or a system model. Themachine learning data analysis circuit may be structured to learnreceived output data patterns based on the outcome or the state. Themonitoring system may keep or modify operational parameters or equipmentbased on the predicted outcome or the state. The data collection circuitcollects data points from one or more of the plurality of sensors basedon the learned received output data patterns, the outcome, or the state.The data collection circuit may change a data storage technique for theoutput data based on the learned received output data patterns, theoutcome, or the state. The data collection circuit may change a datapresentation mode or manner based on the learned received output datapatterns, the outcome, or the state. The data collection circuit mayapply one or more filters (low pass, high pass, band pass, etc.) to theoutput data. The data collection circuit may adjust the weights/biasesof the machine learning data analysis circuit, such as where theadjustment is in response to the learned received output data patterns.The monitoring system may remove, or re-task under-utilized equipmentbased on one or more of the learned received output data patterns, theoutcome, or the state. The machine learning data analysis circuit mayinclude a neural network expert system. The machine learning dataanalysis circuit may be structured to learn received output datapatterns indicative of progress/alignment with one or more goals orguidelines, such as where progress or alignment of each goal orguideline is determined by a different subset of the plurality ofsensors. The machine learning data analysis circuit may be structured tolearn received output data patterns indicative of an unknown variable.The machine learning data analysis circuit may be structured to learnreceived output data patterns indicative of a preferred input sensoramong available input sensors. The machine learning data analysiscircuit may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof. The output data from the vibration sensors may form a vibrationfingerprint, such as where the vibration fingerprint includes one ormore of a frequency, a spectrum, a velocity, a peak location, a wavepeak shape, a waveform shape, a wave envelope shape, an acceleration, aphase information, and a phase shift. The data collection circuit mayapply a rule regarding how many parameters of the vibration fingerprintto match or the standard deviation for the match in order to identify amatch between the output data and the learned received output datapattern. The state may be one of a normal operation, a maintenancerequired, a failure, or an imminent failure. The monitoring system maytrigger an alert based on the predicted outcome or state. The monitoringsystem may shut down equipment, component, or line based on thepredicted outcome or state. The monitoring system may initiatemaintenance, lubrication, or alignment based on the predicted outcome orstate. The monitoring system may deploy a field technician based on thepredicted outcome or state. The monitoring system may recommend avibration absorption or dampening device based on the predicted outcomeor state. The monitoring system may modify a process to utilize backupequipment or a component based on the predicted outcome or state. Themonitoring system may modify a process to preserve products or reactantsbased on the predicted outcome or state. The monitoring system maygenerate or modify a maintenance schedule based on the predicted outcomeor state. The system may be distributed between the data collector and aremote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein themonitoring system is structured to determine if the output data matchesa learned received output data pattern and keep or modify operationalparameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection circuit structured tocollect output data from a plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternspredictive of at least one of an outcome and a state, wherein the outputdata from the vibration sensors forms a vibration fingerprint. Inembodiments, the vibration fingerprint may comprise one or more of afrequency, a spectrum, a velocity, a peak location, a wave peak shape, awaveform shape, a wave envelope shape, an acceleration, a phaseinformation, and a phase shift. The data collection circuit may apply arule regarding how many parameters of the vibration fingerprint to matchor the standard deviation for the match in order to identify a matchbetween the output data and the learned received output data pattern.The monitoring system may be structured to determine if the output datamatches a learned received output data pattern and keep or modifyoperational parameters or equipment based on the determination.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise: a data collection band circuit that identifiesa subset of a plurality of sensors from which to process output data,the sensors selected among vibration sensors, ambient environmentcondition sensors and local sensors for collecting non-vibration dataproximal to a machine in the environment, the plurality of sensorscommunicatively coupled to the data collection band circuit; a datacollection circuit structured to collect the output data from the subsetof plurality of sensors; and a machine learning data analysis circuitstructured to receive the output data and learn received output datapatterns predictive of at least one of an outcome and a state whereinwhen the learned received output data patterns do not reliably predictthe outcome or the state, the data collection band circuit alters atleast one parameter of at least one of the plurality of sensors. Inembodiments, the controller may identify a new data collection bandcircuit based on one or more of the learned received output datapatterns and the outcome or state. The machine learning data analysiscircuit may be further structured to learn received output data patternsindicative of a preferred input data collection band among availableinput data collection bands. The system may be distributed between thedata collection circuit and a remote infrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors, the sensors selected amongvibration sensors, ambient environment condition sensors and localsensors for collecting non-vibration data proximal to a machine in theenvironment and being communicatively coupled to the data collectioncircuit, wherein the output data from the vibration sensors is in theform of a vibration fingerprint; a data structure comprising a pluralityof vibration fingerprints and associated outcomes; and a machinelearning data analysis circuit structured to receive the output data andlearn received output data patterns predictive of an outcome or a statebased on processing of the vibration fingerprints. The machine learningdata analysis circuit may be seeded with one of the plurality ofvibration fingerprints from the data structure. The data structure maybeupdated if a changed parameter resulted in a new vibration fingerprintor if a predicted outcome did not occur in the absence of mitigation.The data structure may be updated when the learned received output datapatterns do not reliably predict the outcome or the state. The systemmay be distributed between the data collection circuit and a remoteinfrastructure.

In embodiments, a monitoring system for data collection in an industrialenvironment may comprise a data collection circuit structured to collectoutput data from the plurality of sensors selected among vibrationsensors, ambient environment condition sensors and local sensors forcollecting non-vibration data proximal to a machine in the environment,the plurality of sensors communicatively coupled to the data collectioncircuit, wherein the output data from the plurality of sensors is in theform of a noise pattern; a data structure comprising a plurality ofnoise patterns and associated outcomes; and a machine learning dataanalysis circuit structured to receive the output data and learnreceived output data patterns predictive of an outcome or a state basedon processing of the noise patterns.

An example system for data collection in an industrial environmentincludes an industrial system having a number of components, and anumber of sensors wherein each of the sensors is operatively coupled toat least one of the components. The example system further includes asensor communication circuit that interprets a number of sensor datavalues in response to a sensed parameter group, a pattern recognitioncircuit that determines a recognized pattern value in response to atleast a portion of the sensor data values, and a sensor learning circuitthat updates the sensed parameter group in response to the recognizedpattern value. The example sensor communication circuit further adjuststhe interpreting the sensor data values in response to the updatedsensed parameter group.

Certain further aspects of an example system are described following,any one or more of which may be present in certain embodiments. Anexample system includes the sensed parameter group being a fused numberof sensors, and where the recognized pattern value further includes asecondary value including a value determined in response to the fusednumber of sensors. An example system further includes the patternrecognition circuit and the sensor learning circuit iterativelyperforming the determining the recognized pattern value and the updatingthe sensed parameter group to improve a sensing performance value. Anexample system further includes the sensing performance value include adetermination of one or more of the following: a signal-to-noiseperformance for detecting a value of interest in the industrial system;a network utilization of the sensors in the industrial system; aneffective sensing resolution for a value of interest in the industrialsystem; a power consumption value for a sensing system in the industrialsystem, the sensing system including the sensors; a calculationefficiency for determining the secondary value; an accuracy and/or aprecision of the secondary value; a redundancy capacity for determiningthe secondary value; and/or a lead time value for determining thesecondary value. Example and non-limiting calculation efficiency valuesinclude one or more determinations such as: processor operations todetermine the secondary value; memory utilization for determining thesecondary value; a number of sensor inputs from the number of sensorsfor determining the secondary value; and/or supporting data long-termstorage for supporting the secondary value.

An example system includes one or more, or all, of the sensors as analogsensors and/or as remote sensors. An example system includes thesecondary value being a value such as: a virtual sensor output value; aprocess prediction value; a process state value; a component predictionvalue; a component state value; and/or a model output value having thesensor data values from the fused number of sensors as an input. Anexample system includes the fused number of sensors being one or more ofthe combinations of sensors such as: a vibration sensor and atemperature sensor; a vibration sensor and a pressure sensor; avibration sensor and an electric field sensor; a vibration sensor and aheat flux sensor; a vibration sensor and a galvanic sensor; and/or avibration sensor and a magnetic sensor.

An example sensor learning circuit further updates the sensed parametergroup by performing an operation such as: updating a sensor selection ofthe sensed parameter group; updating a sensor sampling rate of at leastone sensor from the sensed parameter group; updating a sensor resolutionof at least one sensor from the sensed parameter group; updating astorage value corresponding to at least one sensor from the sensedparameter group; updating a priority corresponding to at least onesensor from the sensed parameter group; and/or updating at least one ofa sampling rate, sampling order, sampling phase, and/or a network pathconfiguration corresponding to at least one sensor from the sensedparameter group. An example pattern recognition circuit furtherdetermines the recognized pattern value by performing an operation suchas: determining a signal effectiveness of at least one sensor of thesensed parameter group and the updated sensed parameter group relativeto a value of interest; determining a sensitivity of at least one sensorof the sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive confidenceof at least one sensor of the sensed parameter group and the updatedsensed parameter group relative to the value of interest; determining apredictive delay time of at least one sensor of the sensed parametergroup and the updated sensed parameter group relative to the value ofinterest; determining a predictive accuracy of at least one sensor ofthe sensed parameter group and the updated sensed parameter grouprelative to the value of interest; determining a predictive precision ofat least one sensor of the sensed parameter group and the updated sensedparameter group relative to the value of interest; and/or updating therecognized pattern value in response to external feedback. Example andnon-limiting values of interest include: a virtual sensor output value;a process prediction value; a process state value; a componentprediction value; a component state value; and/or a model output valuehaving the sensor data values from the fused plurality of sensors as aninput.

An example pattern recognition circuit further accesses cloud-based dataincluding a second number of sensor data values, the second number ofsensor data values corresponding to at least one offset industrialsystem. An example sensor learning circuit further accesses thecloud-based data including a second updated sensor parameter groupcorresponding to the at least one offset industrial system.

An example procedure for data collection in an industrial environmentincludes an operation to provide a number of sensors to an industrialsystem including a number of components, each of the number of sensorsoperatively coupled to at least one of the number of components, anoperation to interpret a number of sensor data values in response to asensed parameter group, the sensed parameter group including a fusednumber of sensors from the number of sensors, an operation to determinea recognized pattern value including a secondary value determined inresponse to the number of sensor data values, an operation to update thesensed parameter group in response to the recognized pattern value, andan operation to adjust the interpreting the number of sensor data valuesin response to the updated sensed parameter group.

Certain further aspects of an example procedure are described following,any one or more of which may be included in certain embodiments. Anexample procedure includes an operation to iteratively perform thedetermining the recognized pattern value and the updating the sensedparameter group to improve a sensing performance value, wheredetermining the sensing performance value includes an least oneoperation for determining a value, such as determining: asignal-to-noise performance for detecting a value of interest in theindustrial system; a network utilization of the plurality of sensors inthe industrial system; an effective sensing resolution for a value ofinterest in the industrial system; a power consumption value for asensing system in the industrial system, the sensing system includingthe plurality of sensors; a calculation efficiency for determining thesecondary value; an accuracy and/or a precision of the secondary value;a redundancy capacity for determining the secondary value; and/or a leadtime value for determining the secondary value.

An example procedure includes an operation to update the sensedparameter group comprised by performing at least one operation such as:updating a sensor selection of the sensed parameter group; updating asensor sampling rate of at least one sensor from the sensed parametergroup; updating a sensor resolution of at least one sensor from thesensed parameter group; updating a storage value corresponding to atleast one sensor from the sensed parameter group; updating a prioritycorresponding to at least one sensor from the sensed parameter group;and/or updating at least one of a sampling rate, sampling order,sampling phase, and a network path configuration corresponding to atleast one sensor from the sensed parameter group. An example procedureincludes determining the recognized pattern value by performing at leastone operation such as: determining a signal effectiveness of at leastone sensor of the sensed parameter group and the updated sensedparameter group relative to a value of interest; determining asensitivity of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive confidence of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; determining a predictive delay time of at least onesensor of the sensed parameter group and the updated sensed parametergroup relative to the value of interest; determining a predictiveaccuracy of at least one sensor of the sensed parameter group and theupdated sensed parameter group relative to the value of interest;determining a predictive precision of at least one sensor of the sensedparameter group and the updated sensed parameter group relative to thevalue of interest; and/or updating the recognized pattern value inresponse to external feedback.

The term industrial system (and similar terms) as utilized herein shouldbe understood broadly. Without limitation to any other aspect ordescription of the present disclosure, an industrial system includes anylarge scale process system, mechanical system, chemical system, assemblyline, oil and gas system (including, without limitation, production,transportation, exploration, remote operations, offshore operations,and/or refining), mining system (including, without limitation,production, exploration, transportation, remote operations, and/orunderground operations), rail system (yards, trains, shipments, etc.),construction, power generation, aerospace, agriculture, food processing,and/or energy generation. Certain components may not be consideredindustrial individually, but may be considered industrially in anaggregated system—for example a single fan, motor, and/or engine may benot an industrial system, but may be a part of a larger system and/or beaccumulated with a number of other similar components to be consideredan industrial system and/or a part of an industrial system. In certainembodiments, a system may be considered an industrial system for somepurposes but not for other purposes—for example a large data server farmmay be considered an industrial system for certain sensing operations,such as temperature detection, vibration, or the like, but not anindustrial system for other sensing operations such as gas composition.Additionally, in certain embodiments, otherwise similar looking systemsmay be differentiated in determining whether such system are industrialsystems, and/or which type of industrial system. For example, one dataserver farm may not, at a given time, have process stream flow ratesthat are critical to operation, while another data server farm may haveprocess stream flow rates that are critical to operation (e.g., acoolant flow stream), and accordingly one data farm server may be anindustrial system for a data collection and/or sensing improvementprocess or system, while the other is not. Accordingly, the benefits ofthe present disclosure may be applied in a wide variety of systems, andany such systems may be considered an industrial system herein, while incertain embodiments a given system may not be considered an industrialsystem herein. One of skill in the art, having the benefit of thedisclosure herein and knowledge about a contemplated system ordinarilyavailable to that person, can readily determine which aspects of thepresent disclosure will benefit a particular system, how to combineprocesses and systems from the present disclosure to enhance operationsof the contemplated system. Certain considerations for the person ofskill in the art, in determining whether a contemplated system is anindustrial system and/or whether aspects of the present disclosure canbenefit or enhance the contemplated system include, without limitation:the accessibility of portions of the system to positioning sensingdevices; the sensitivity of the system to capital costs (e.g., initialinstallation) and operating costs (e.g., optimization of processes,reduction of power usage); the transmission environment of the system(e.g., availability of broadband internet; satellite coverage; wirelesscellular access; the electro-magnetic (“EM”) environment of the system;the weather, temperature, and environmental conditions of the system;the availability of suitable locations to run wires, network lines, andthe like; the presence and/or availability of suitable locations fornetwork infrastructure, router positioning, and/or wireless repeaters);the availability of trained personnel to interact with computingdevices; the desired spatial, time, and/or frequency resolution ofsensed parameters in the system; the degree to which a system or processis well understood or modeled; the turndown ratio in system operations(e.g., high load differential to low load; high flow differential to lowflow; high temperature operation differential to low temperatureoperation); the turndown ratio in operating costs (e.g., effects ofpersonnel costs based on time (day, season, etc.); effects of powerconsumption cost variance with time, throughput, etc.); the sensitivityof the system to failure, down-time, or the like; the remoteness of thecontemplated system (e.g., transport costs, time delays, etc.); and/orqualitative scope of change in the system over the operating cycle(e.g., the system runs several distinct processes requiring a variablesensing environment with time; time cycle and nature of changes such asperiodic, event driven, lead times generally available, etc.). Whilespecific examples of industrial systems and considerations are describedherein for purposes of illustration, any system benefitting from thedisclosures herein, and any considerations understood to one of skill inthe art having the benefit of the disclosures herein, are specificallycontemplated within the scope of the present disclosure.

The term sensor (and similar terms) as utilized herein should beunderstood broadly. Without limitation to any other aspect ordescription of the present disclosure, sensor includes any deviceconfigured to provide a sensed value representative of a physical value(e.g., temperature, force, pressure) in a system, or representative of aconceptual value in a system at least having an ancillary relationshipto a physical value (e.g., work, state of charge, frequency, phase,etc.).

Example and non-limiting sensors include vibration, acceleration, noise,pressure, force, position, location, velocity, displacement,temperature, heat flux, speed, rotational speed (e.g., a tachometer),motion, accelerometers, magnetic field, electrical field, galvanic,current, flow (gas, fluid, heat, particulates, particles, etc.), level,proximity, gas composition, fluid composition, toxicity, corrosiveness,acidity, pH, humidity, hygrometer measures, moisture, density (bulk orspecific), ultrasound, imaging, analog, and/or digital sensors. The listof sensed values is a non-limiting example, and the benefits of thepresent disclosure in many applications can be realized independent ofthe sensor type, while in other applications the benefits of the presentdisclosure may be dependent upon the sensor type.

The sensor type and mechanism for detection may be any type of sensorunderstood in the art. Without limitation, an accelerometer may be anytype and scaling, for example 500 mV per g (1 g=9.8 m/s²), 100 mV, 1 Vper g, 5 V per g, 10 V per g, 10 MV per g, as well as any frequencycapability. It will be understood for accelerometers, and for all sensortypes, that the scaling and range may be competing (e.g., in a fixed-bitor low bit A/D system), and/or selection of high resolution scaling witha large range may drive up sensor and/or computing costs, which may beacceptable in certain embodiments, and may be prohibitive in otherembodiments. Example and non-limiting accelerometers includepiezo-electric devices, high resolution and sampling speed positiondetection devices (e.g., laser based devices), and/or detection of otherparameters (strain, force, noise, etc.) that can be correlated toacceleration and/or vibration. Example and non-limiting proximity probesinclude electro-magnetic devices (e.g., Hall effect, VariableReluctance, etc.), a sleeve/oil film device, and/or determination ofother parameters than can be correlated to proximity. An examplevibration sensor includes a tri-axial probe, which may have highfrequency response (e.g., scaling of 100 MV/g). Example and non-limitingtemperature sensors include thermistors, thermocouples, and/or opticaltemperature determination.

A sensor may, additionally or alternatively, provide a processed value(e.g., a de-bounced, filtered, and/or compensated value) and/or a rawvalue, with processing downstream (e.g., in a data collector,controller, plant computer, and/or on a cloud-based data receiver). Incertain embodiments, a sensor provides a voltage, current, data file(e.g., for images), or other raw data output, and/or a sensor provides avalue representative of the intended sensed measurement (e.g., atemperature sensor may communicate a voltage or a temperature value).Additionally or alternatively, a sensor may communicate wirelessly,through a wired connection, through an optical connection, or by anyother mechanism. The described examples of sensor types and/orcommunication parameters are non-limiting examples for purposes ofillustration.

Additionally or alternatively, in certain embodiments, a sensor is adistributed physical device—for example where two separate sensingelements coordinate to provide a sensed value (e.g., a position sensingelement and a mass sensing element may coordinate to provide anacceleration value). In certain embodiments, a single physical devicemay form two or more sensors, and/or parts of more than one sensor. Forexample, a position sensing element may form a position sensor and avelocity sensor, where the same physical hardware provides the senseddata for both determinations.

The term smart sensor, smart device (and similar terms) as utilizedherein should be understood broadly. Without limitation to any otheraspect or description of the present disclosure, a smart sensor includesany sensor and aspect thereof as described throughout the presentdisclosure. A smart sensor includes an increment of processing reflectedin the sensed value communicated by the sensor, including at least basicsensor processing (e.g., de-bouncing, filtering, compensation,normalization, and/or output limiting), more complex compensations(e.g., correcting a temperature value based on known effects of currentenvironmental conditions on the sensed temperature value, common mode orother noise removal, etc.), a sensing device that provides the sensedvalue as a network communication, and/or a sensing device thataggregates a number of sensed values for communication (e.g., multiplesensors on a device communicated out in a parseable or deconvolutablemanner or as separate messages; multiple sensors providing a value to asingle smart sensor, which relays sensed values on to a data collector,controller, plant computer, and/or cloud-based data receiver). The useof the term smart sensor is for purposes of illustration, and whether asensor is a smart sensor can depend upon the context and thecontemplated system, and can be a relative description compared to othersensors in the contemplated system. Thus, a given sensor havingidentical functionality may be a smart sensor for the purposes of onecontemplated system, and just a sensor for the purposes of anothercontemplated system, and/or may be a smart sensor in a contemplatedsystem during certain operating conditions, and just a sensor for thepurposes of the same contemplated system during other operatingconditions.

The terms sensor fusion, fused sensors, and similar terms, as utilizedherein, should be understood broadly, except where context indicatesotherwise, without limitation to any other aspect or description of thepresent disclosure. A sensor fusion includes a determination of secondorder data from sensor data, and further includes a determination ofsecond order data from sensor data of multiple sensors, includinginvolving multiplexing of streams of data, combinations of batches ofdata, and the like from the multiple sensors. Second order data includesa determination about a system or operating condition beyond that whichis sensed directly. For example, temperature, pressure, mixing rate, andother data may be analyzed to determine which parameters areresult-effective on a desired outcome (e.g., a reaction rate). Thesensor fusion may include sensor data from multiple sources, and/orlongitudinal data (e.g., taken over a period of time, over the course ofa process, and/or over an extent of components in a plant—for exampletracking a number of assembled parts, a virtual slug of fluid passingthrough a pipeline, or the like). The sensor fusion may be performed inreal-time (e.g., populating a number of sensor fusion determinationswith sensor data as a process progresses), off-line (e.g., performed ona controller, plant computer, and/or cloud-based computing device),and/or as a post-processing operation (e.g., utilizing historical data,data from multiple plants or processes, etc.). In certain embodiments, asensor fusion includes a machine pattern recognition operation—forexample where an outcome of a process is given to the machine and/ordetermined by the machine, and the machine pattern recognition operationdetermines result-effective parameters from the detected sensor valuespace to determine which operating conditions were likely to be thecause of the outcome and/or the off-nominal result of the outcome (e.g.,process was less effective or more effective than nominal, failed,etc.). In certain embodiments, the outcome may be a quantitative outcome(e.g., 20% more product was produced than a nominal run) or aqualitative outcome (e.g., product quality was unacceptable, component Xof the contemplated system failed during the process, component X of thecontemplated system required a maintenance or service event, etc.).

In certain embodiments, a sensor fusion operation is iterative orrecursive—for example an estimated set of result effective parameters isupdated after the sensor fusion operation, and a subsequent sensorfusion operation is performed on the same data or another data set withan updated set of the result effective parameters. In certainembodiments, subsequent sensor fusion operations include adjustments tothe sensing scheme—for example higher resolution detections (e.g., intime, space, and/or frequency domains), larger data sets (and consequentcommitment of computing and/or networking resources), changes in sensorcapability and/or settings (e.g., changing an A/D scaling, range,resolution, etc.; changing to a more capable sensor and/or more capabledata collector, etc.) are performed for subsequent sensor fusionoperations. In certain embodiments, the sensor fusion operationdemonstrates improvements to the contemplated system (e.g., productionquantity, quality, and/or purity, etc.) such that expenditure ofadditional resources to improve the sensing scheme are justified. Incertain embodiments, the sensor fusion operation provides forimprovement in the sensing scheme without incremental cost—for exampleby narrowing the number of result effective parameters and therebyfreeing up system resources to provide greater resolution, samplingrates, etc., from hardware already present in the contemplated system.In certain embodiments, iterative and/or recursive sensor fusion isperformed on the same data set, a subsequent data set, and/or ahistorical data set. For example, high resolution data may already bepresent in the system, and a first sensor fusion operation is performedwith low resolution data (e.g., sampled from the high resolution dataset), such as to allow for completion of sensor fusion processingoperations within a desired time frame, within a desired processor,memory, and/or network utilization, and/or to allow for checking a largenumber of variables as potential result effective parameters. In afurther example, a greater number of samples from the high resolutiondata set may be utilized in a subsequent sensor fusion operation inresponse to confidence that improvements are present, narrowing of thepotential result effective variables, and/or a determination that higherresolution data is required to determine the result effective parametersand/or effective values for such parameters.

The described operations and aspects for sensor fusion are non-limitingexamples, and one of skill in the art, having the benefit of thedisclosures herein and information ordinarily available about acontemplated system, can readily design a system to utilize and/orbenefit from a sensor fusion operation. Certain considerations for asystem to utilize and/or benefit from a sensor fusion operation include,without limitation: the number of components in the system; the cost ofcomponents in the system; the cost of maintenance and/or down-time forthe system; the value of improvements in the system (productionquantity, quality, yield, etc.); the presence, possibility, and/orconsequences of undesirable system outcomes (e.g., side products,thermal and/or luminary events, environmental benefits or consequences,hazards present in the system); the expense of providing a multiplicityof sensors for the system; the complexity between system inputs andsystem outputs; the availability and cost of computing resources (e.g.,processing, memory, and/or communication throughput); the size/scale ofthe contemplated system and/or the ability of such a system to generatestatistically significant data; whether offset systems exist, includingwhether data from offset systems is available and whether combining datafrom offset systems will generate a statistically improved data setrelative to the system considered alone; and/or the cost of upgrading,improving, or changing a sensing scheme for the contemplated system. Thedescribed considerations for a contemplated system that may benefit fromor utilize a sensor fusion operation are non-limiting illustrations.

Certain systems, processes, operations, and/or components are describedin the present disclosure as “offset systems” or the like. An offsetsystem is a system distinct from a contemplated system, but havingrelevance to the contemplated system. For example, a contemplatedrefinery may have an “offset refinery,” which may be a refinery operatedby a competitor, by a same entity operating the contemplated refinery,and/or a historically operated refinery that no longer exists. Theoffset refinery bears some relevant relationship to the contemplatedrefinery, such as utilizing similar reactions, process flows, productionvolumes, feed stock, effluent materials, or the like. A system which isan offset system for one purpose may not be an offset system for anotherpurpose. For example, a manufacturing process utilizing conveyor beltsand similar motors may be an offset process for a contemplatedmanufacturing process for the purpose of tracking product movement,understanding motor operations and failure modes, or the like, but maynot be an offset process for product quality if the products beingproduced have distinct quality outcome parameters. Any industrial systemcontemplated herein may have an offset system for certain purposes. Oneof skill in the art, having the benefit of the present disclosure andinformation ordinarily available for a contemplated system, can readilydetermine what is disclosed by an offset system or offset aspect of asystem.

Any one or more of the terms computer, computing device, processor,circuit, and/or server include a computer of any type, capable to accessinstructions stored in communication thereto such as upon anon-transient computer readable medium, whereupon the computer performsoperations of systems or methods described herein upon executing theinstructions. In certain embodiments, such instructions themselvescomprise a computer, computing device, processor, circuit, and/orserver. Additionally or alternatively, a computer, computing device,processor, circuit, and/or server may be a separate hardware device, oneor more computing resources distributed across hardware devices, and/ormay include such aspects as logical circuits, embedded circuits,sensors, actuators, input and/or output devices, network and/orcommunication resources, memory resources of any type, processingresources of any type, and/or hardware devices configured to beresponsive to determined conditions to functionally execute one or moreoperations of systems and methods herein.

Certain operations described herein include interpreting, receiving,and/or determining one or more values, parameters, inputs, data, orother information. Operations including interpreting, receiving, and/ordetermining any value parameter, input, data, and/or other informationinclude, without limitation: receiving data via a user input; receivingdata over a network of any type; reading a data value from a memorylocation in communication with the receiving device; utilizing a defaultvalue as a received data value; estimating, calculating, or deriving adata value based on other information available to the receiving device;and/or updating any of these in response to a later received data value.In certain embodiments, a data value may be received by a firstoperation, and later updated by a second operation, as part of thereceiving a data value. For example, when communications are down,intermittent, or interrupted, a first operation to interpret, receive,and/or determine a data value may be performed, and when communicationsare restored an updated operation to interpret, receive, and/ordetermine the data value may be performed.

Certain logical groupings of operations herein, for example methods orprocedures of the current disclosure, are provided to illustrate aspectsof the present disclosure. Operations described herein are schematicallydescribed and/or depicted, and operations may be combined, divided,re-ordered, added, or removed in a manner consistent with the disclosureherein. It is understood that the context of an operational descriptionmay require an ordering for one or more operations, and/or an order forone or more operations may be explicitly disclosed, but the order ofoperations should be understood broadly, where any equivalent groupingof operations to provide an equivalent outcome of operations isspecifically contemplated herein. For example, if a value is used in oneoperational step, the determining of the value may be required beforethat operational step in certain contexts (e.g., where the time delay ofdata for an operation to achieve a certain effect is important), but maynot be required before that operation step in other contexts (e.g.,where usage of the value from a previous execution cycle of theoperations would be sufficient for those purposes). Accordingly, incertain embodiments an order of operations and grouping of operations asdescribed is explicitly contemplated herein, and in certain embodimentsre-ordering, subdivision, and/or different grouping of operations isexplicitly contemplated herein.

Referencing FIG. 73, an example procedure 11038 includes an operation11040 to provide a number of sensors to an industrial system including anumber of components, each of the number of sensors operatively coupledto at least one of the number of components, an operation 11042 tointerpret a number of sensor data values in response to a sensedparameter group, the sensed parameter group including at least onesensor of the number of sensors, an operation 11044 to determine arecognized pattern value in response to at least a portion of the numberof sensor data values, and an operation 11046 to provide a systemcharacterization value for the industrial system in response to therecognized pattern value.

An example procedure 11038 further includes the operation 11046 toprovide the system characterization value by performing an operationsuch as: determining a predicted outcome for a process associated withthe industrial system; determining a predicted future state for aprocess associated with the industrial system; determining a predictedoff-nominal operation for the process associated with the industrialsystem; determining a prediction value for one of the plurality ofcomponents; determining a future state value for one of the plurality ofcomponents; determining an anticipated maintenance health stateinformation for one of the plurality of components; determining apredicted maintenance interval for at least one of the plurality ofcomponents; determining a predicted off-nominal operation for one of theplurality of components; determining a predicted fault operation for oneof the plurality of components; determining a predicted exceedance valuefor one of the plurality of components; and/or determining a predictedsaturation value for one of the plurality of sensors.

An example procedure 11038 includes an operation 11050 to interpretexternal data and/or cloud-based data, and where the operation 11044 todetermine the recognized pattern value is further in response to theexternal data and/or the cloud-based data. An example procedure 11038includes an operation to iteratively improve pattern recognitionoperations in response to the external data and/or the cloud-based data,for example by operation 11048 to adjust the operation 11042interpreting sensor values, such as by updating the sensed parametergroup. The operation to iteratively improve pattern recognition mayfurther include repeating operations 11042 through 11048, periodically,at selected intervals, in response to a system change, and/or inresponse to a prediction value of a component, process, or the system.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to atleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the industrial system in response to the recognized patternvalue. In embodiments, a characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted outcome for a process associated with theindustrial system; a predicted future state for a process associatedwith the industrial system; and a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may include at least one characterization valueselected from the characterization values consisting of: a predictionvalue for one of the plurality of components; a future state value forone of the plurality of components; an anticipated maintenance healthstate information for one of the plurality of components; and apredicted maintenance interval for at least one of the plurality ofcomponents. The system characterization value may include at least onecharacterization value selected from the characterization valuesconsisting of: a predicted off-nominal operation for one of theplurality of components; a predicted fault operation for one of theplurality of components; and a predicted exceedance value for one of theplurality of components. The system characterization value may include apredicted saturation value for one of the plurality of sensors. A systemcollaboration circuit may be included that is structured to interpretexternal data, and wherein the pattern recognition circuit is furtherstructured to determine the recognized pattern value further in responseto the external data. The pattern recognition circuit may be furtherstructured to iteratively improve pattern recognition operations inresponse to the external data. The external data may include at leastone of: an indicated component maintenance event; an indicated componentfailure event; an indicated component wear value; an indicated componentoperational exceedance value; and an indicated fault value. The externaldata may include at least one of: an indicated process failure value; anindicated process success value; an indicated process outcome value; andan indicated process operational exceedance value. The external data mayinclude an indicated sensor saturation value. A system collaborationcircuit may be included that is structured to interpret cloud-based datacomprising a second plurality of sensor data values, the secondplurality of sensor data values corresponding to at least one offsetindustrial system, and wherein the pattern recognition circuit isfurther structured to determine the recognized pattern value further inresponse to the cloud-based data. The pattern recognition circuit may befurther structured to iteratively improve pattern recognition operationsin response to the cloud-based data. The sensed parameter group mayinclude a triaxial vibration sensor. The sensed parameter group mayinclude a vibration sensor and a second sensor that is not a vibrationsensor, such as where the second sensor comprises a digital sensor. Thesensed parameter group may include a plurality of analog sensors.

In embodiments, a method may comprise: providing a plurality of sensorsto an industrial system comprising a plurality of components, each ofthe plurality of sensors operatively coupled to at least one of theplurality of components; interpreting a plurality of sensor data valuesin response to a sensed parameter group, the sensed parameter groupcomprising at least one sensor of the plurality of sensors; determininga recognized pattern value in response to at least a portion of theplurality of sensor data values; and providing a system characterizationvalue for the industrial system in response to the recognized patternvalue. The system characterization value may be provided by performingat least one operation selected from the operations consisting of:determining a prediction value for one of the plurality of components;determining a future state value for one of the plurality of components;determining an anticipated maintenance health state information for oneof the plurality of components; and determining a predicted maintenanceinterval for at least one of the plurality of components. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The systemcharacterization value may be provided by performing at least oneoperation selected from the operations consisting of: determining apredicted off-nominal operation for one of the plurality of components;determining a predicted fault operation for one of the plurality ofcomponents; and determining a predicted exceedance value for one of theplurality of components. The system characterization value may beprovided by determining a predicted saturation value for one of theplurality of sensors. Determining the recognized pattern value may befurther in response to the external data. Iteratively improving patternrecognition operations may be provided in response to the external data.Interpreting the external data may include at least one operationselected from the operations consisting of: interpreting an indicatedcomponent maintenance event; interpreting an indicated component failureevent; interpreting an indicated component wear value; interpreting anindicated component operational exceedance value; and interpreting anindicated fault value. Interpreting the external data may include atleast one operation selected from the operations consisting of:interpreting an indicated process success value; interpreting anindicated process failure value; interpreting an indicated processoutcome value; and interpreting an indicated process operationalexceedance value. Interpreting the external data may includeinterpreting an indicated sensor saturation value. Interpretingcloud-based data may include a second plurality of sensor data values,the second plurality of sensor data values corresponding to at least oneoffset industrial system, and wherein determining the recognized patternvalue is further in response to the cloud-based data. Iterativelyimproving pattern recognition operations may be provided in response tothe cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: an industrial system comprising a plurality ofcomponents, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a means for determining arecognized pattern value in response to at least a portion of theplurality of sensor data values; and a means for providing a systemcharacterization value for the industrial system in response to therecognized pattern value. The means for providing the systemcharacterization value may comprise a means for performing at least oneoperation selected from the operations consisting of: determining apredicted outcome for a process associated with the industrial system;determining a predicted future state for a process associated with theindustrial system; and determining a predicted off-nominal operation forthe process associated with the industrial system. The means forproviding the system characterization value may include a means forperforming at least one operation selected from the operationsconsisting of: determining a prediction value for one of the pluralityof components; determining a future state value for one of the pluralityof components; determining an anticipated maintenance health stateinformation for one of the plurality of components; and determining apredicted maintenance interval for at least one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for performing at least one operation selected fromthe operations consisting of: determining a predicted off-nominaloperation for one of the plurality of components; determining apredicted fault operation for one of the plurality of components; anddetermining a predicted exceedance value for one of the plurality ofcomponents. The means for providing the system characterization valuemay include a means for determining a predicted saturation value for oneof the plurality of sensors. A system collaboration circuit may beprovided that is structured to interpret external data, and wherein themeans for determining the recognized pattern value determines therecognized pattern value further in response to the external data. Ameans for iteratively improving pattern recognition operations may beprovided in response to the external data. The external data may includeat least one of: an indicated process success value; an indicatedprocess failure value; and an indicated process outcome value. Theexternal data may include at least one of: an indicated componentmaintenance event; an indicated component failure event; and anindicated component wear value. The external data may include at leastone of: an indicated process operational exceedance value; an indicatedcomponent operational exceedance value; and an indicated fault value.The external data may include an indicated sensor saturation value. Asystem collaboration circuit may be provided that is structured tointerpret cloud-based data comprising a second plurality of sensor datavalues, the second plurality of sensor data values corresponding to atleast one offset industrial system, and wherein the means fordetermining the recognized pattern value determines the recognizedpattern value further in response to the cloud-based data. A means foriteratively improving pattern recognition operations may be provided inresponse to the cloud-based data.

In embodiments, a system for data collection in an industrialenvironment may comprise: a distillation column comprising a pluralityof components, and a plurality of sensors each operatively coupled to atleast one of the plurality of components; a sensor communication circuitstructured to interpret a plurality of sensor data values in response toa sensed parameter group, the sensed parameter group comprising at leastone sensor of the plurality of sensors; a pattern recognition circuitstructured to determine a recognized pattern value in response to atleast a portion of the plurality of sensor data values; and a systemcharacterization circuit structured to provide a system characterizationvalue for the distillation column in response to the recognized patternvalue. The plurality of components may include a thermodynamic treatmentcomponent, and wherein the system characterization value comprises atleast one value selected from the values consisting of: determining aprediction value for the thermodynamic treatment component; determininga future state value for the thermodynamic treatment component;determining an anticipated maintenance health state information for thethermodynamic treatment component; and determining a process ratelimitation according to a capacity of the thermodynamic treatmentcomponent. The thermodynamic treatment component may include at leastone of a compressor or a boiler.

In embodiments, a system for data collection in an industrialenvironment may comprise: a chemical process system comprising aplurality of components, and a plurality of sensors each operativelycoupled to at least one of the plurality of components; a sensorcommunication circuit structured to interpret a plurality of sensor datavalues in response to a sensed parameter group, the sensed parametergroup comprising at least one sensor of the plurality of sensors; apattern recognition circuit structured to determine a recognized patternvalue in response to at least a portion of the plurality of sensor datavalues; and a system characterization circuit structured to provide asystem characterization value for the chemical process system inresponse to the recognized pattern value. The chemical process systemmay include one of a chemical plant, a pharmaceutical plant, or an oilrefinery. The system characterization value may include at least onevalue selected from the values consisting of: a separation process valuecomprising at least one of a capacity value or a purity value; a sidereaction process value comprising a side reaction rate value; and athermodynamic treatment value comprising one of a capability, acapacity, and an anticipated maintenance health for a thermodynamictreatment component.

A system for data collection in an industrial environment, the systemcomprising: an irrigation system comprising a plurality of componentsincluding a pump, and a plurality of sensors each operatively coupled toat least one of the plurality of components; a sensor communicationcircuit structured to interpret a plurality of sensor data values inresponse to a sensed parameter group, the sensed parameter groupcomprising at least one sensor of the plurality of sensors; a patternrecognition circuit structured to determine a recognized pattern valuein response to at least a portion of the plurality of sensor datavalues; and a system characterization circuit structured to provide asystem characterization value for the irrigation system in response tothe recognized pattern value. The system characterization value mayinclude at least one of an anticipated maintenance health value for thepump and a future state value for the pump. The pattern recognitioncircuit may determine an off-nominal process condition in response tothe at least a portion of the plurality of sensor data values, andwherein the sensor communication circuit is further structured to changethe sensed parameter group in response to the off-nominal processcondition. The off-nominal process condition may include an indicationof below normal water feed availability, and wherein the updated sensedparameter group comprises at least one sensor selected from the sensorsconsisting of: a water level sensor, a humidity sensor, and an auxiliarywater level sensor.

As described elsewhere herein, feedback to various intelligent and/orexpert systems, control systems (including remote and local systems,autonomous systems, and the like), and the like, which may compriserule-based systems, model-based systems, artificial intelligence (AI)systems (including neural nets, self-organizing systems, and othersdescribed throughout this disclosure), and various combinations andhybrids of those (collectively referred to herein as the “expert system”except where context indicates otherwise), may include a wide range ofinformation, including measures such as utilization measures, efficiencymeasures (e.g., power, financial such as reduction of costs), measuresof success in prediction or anticipation of states (e.g., avoidance andmitigation of faults), productivity measures (e.g., workflow), yieldmeasures, profit measures, and the like, as described herein. Inembodiments feedback to the expert system may be industry-specific,domain-specific, factory-specific, machine-specific and the like.

Industry-specific feedback for the expert system may be offered by athird party, such as a repair and maintenance organization,manufacturer, one or more consortia, and the like, or may be generatedby one or more elements of the subject system itself. Industry-specificfeedback may be aggregated, such as into one or more data structures,wherein the data are aggregated at the component level, equipment level,factory/installation level, and/or industry level. Users of the datastructure(s) may access data at any level (e.g., component, equipment,factory, industry, etc.) Users may search the data structure(s) forindicators/predictors based on or filtered by system conditions specificto their need, or update an indicator/predictor with proprietary data tocustomize the data structure to their industry. In embodiments, theexpert system may be seeded with industry-specific feedback, such as ina deep learning fashion, to provide an anticipated outcome or stateand/or to perform actions to optimize specific machines, devices,components, processes, and the like.

In embodiments, feedback provided to the expert system may be used inone or more smart bands to predict progress towards one or more goals.The expert system may use the feedback to determine a modification,alteration, addition, change, or the like to one or more components ofthe system that provided the feedback, as described elsewhere herein.Based on the industry-specific feedback, the expert system may alter aninput, a way of treating or storing an input or output, a sensor orsensors used to provide feedback, an operating parameter, a piece ofequipment used in the system, or any other aspect of the participants inthe industrial system that gave rise to the feedback. As describedelsewhere herein, the expert system may track multiple goals, such aswith one or more smart bands. Industry-specific feedback may be used inor by the smart bands in predicting an outcome or state relating to theone or more goals, and to recommend or instruct a change that isdirected in increasing a likelihood of achieving the outcome or state.

For example, a mixer may be used in a food processing environment or ina chemical processing environment, but the feedback that is relevant inthe food processing plant (e.g., required sterilization temperatures,food viscosity, particle density (e.g., such as measured by an opticalsensor), completion of cooking (e.g., completion of reactions involvedin baking), sanitation (e.g., absence of pathogens) may be differentthan what is relevant in the chemical processing plant (e.g., impellerspeed, velocity vectors, flow rate, absence of high contaminant levels,or the like). This industry specific feedback is useful in optimizingthe operation of the mixer in its particular environment.

In another example, the expert system may use feedback from agriculturalsystems to train a model related to an irrigation system deployed in afield, wherein the industry-specific feedback relates to one or more ofan amount of water used across the industry (e.g., such as measured by aflowmeter), a trend of water usage over a time period (e.g., such asmeasured by a flowmeter), a harvest amount (e.g., such as measured by aweight scale), an insect infestation (e.g., such as identified and/ormeasured by a drone imaging), a plant death (e.g., such as identifiedand/or measured by drone imaging), and the like.

In another example of a fluid flow system (e.g., fan, pump orcompressor) controlling cooling in the manufacturing industry, theexpert system may use feedback from manufacturing of componentsinvolving materials (e.g., polymers) that require cooling during themanufacturing process, such as one or more of quality of output product,strength of output product, flexibility of output product, and the like(e.g., such as measured by a suite of sensors, including densitometer,viscometer, size exclusion chromatograph, and torque meter). If thesensors indicate that the polymer is cooling too quickly during monomerconversion, the expert system may relay an instruction to one or more ofa fan, pump, or compressor in the fluid flow system to decrease anaspect of its operation in order to meet a quality goal.

In another example of a reciprocating compressor operating in a refineryperforming refinery processes (e.g., hydrotreating, hydrocracking,isomerization, reforming), the expert system may use feedback related toone or more of an amount of sulfur, nitrogen and/or aromatics downstreamof the compressor (e.g., such as measured by a near infrared (“IR”)analyzer), the cetane/octane number or smoke point of a product (e.g.,such as with an octane analyzer), the density of a product (e.g., suchas measured by a densitometer), byproduct gas amounts (e.g., such asmeasured by an electrochemical gas sensor), and the like. In thisexample, as feedback is received during isomerization of butane toisobutene by an inline near IR analyzer measuring the amount and/orquality of isobutene, the expert system may determine that theperformance of one or more components of the isomerization system,including the reciprocating compressor, should be altered in order tomeet a production goal.

In another example of a vacuum distillation unit operating in arefinery, the expert system may use feedback related to an amount of rawgasoline recovered (e.g., such as by measuring the volume or compositionof various fractions using IR), boiling point of recovered fractions(e.g., such as with a boiling point analyzer), a vapor cooling rate(e.g., such as measured by thermometer), and the like. In this example,as feedback is received during vacuum distillation to recover diesel, asthe amounts recovered indicate off-nominal rations of production, theexpert system may instruct the vacuum distillation unit to alter afeedstock source and initiate more detailed analysis of the priorfeedstock.

In yet another example of a pipeline in a refinery, the expert systemmay use feedback related to flow type (e.g., bubble, stratified, slug,annular, transition, mist) of hydrocarbon products (e.g., such asmeasured by dye tracing), flow rate, vapor velocity (such as with a flowmeter), vapor shear, and the like. In this example, as feedback isreceived during operation of the pipeline regarding the flow type andits rate, modifications may be recommended by the expert system toimprove the flow through the pipeline.

In still another example of a paddle-type or anchor-type agitator/mixerin a pharmaceutical plant, the expert system may use feedback related todegree of mixing of high-viscosity liquids, heating of medium- tolow-viscosity liquids, a density of the mixture, a growth rate of anorganism in the mixture, and the like. In this example, as feedback isreceived during operation of the agitator that a bacterial growth rateis too high (such as measured with a spectrophotometer), the expertsystem may instruct the agitator to reduce its speed to limit the amountof air being added to the mixture or growth substrate.

In a further example of a pressure reactor in a chemical processingplant, the expert system may use feedback related to a catalyticreaction rate (such as measured by a mass spectrometer), a particledensity (such as measured by a densitometer), a biological growth rate(such as measured by a spectrophotometer), and the like. In thisexample, as feedback is received during operation of the pressurereactor that the particle density and biological growth rate areoff-nominal, the expert system may instruct the pressure reactor tomodify one or more operational parameters, such as a reduction inpressure, an increase in temperature, an increase in volume of thereaction, and the like.

In another example of a gas agitator operating in a chemical processingplant, the expert system may use feedback related to effective densityof a gassed liquid, a viscosity, a gas pressure, and the like, asmeasured by appropriate sensors or equipment. In this example, asfeedback is received during operation of the gas agitator, the expertsystem may instruct the gas agitator to modify one or more operationalparameters, such as to increase or decrease a rate of agitation.

In still another example of a pump blasting liquid type agitator in achemical processing plant, the expert system may use feedback related toa viscosity of a mixture, an optical density of a growth medium, and atemperature of a solution. In this example, as feedback is receivedduring operation of the agitator, the expert system may instruct theagitator to modify one or more operational parameters, such as toincrease or decrease a rate of agitation and/or inject additional heat.

In yet another example of a turbine type agitator in a chemicalprocessing plant, the expert system may use feedback related to avibration noise, a reaction rate of the reactants, a heat transfer, or adensity of a suspension. In this example, as feedback is received duringoperation of the agitator, the expert system may instruct the agitatorto modify one or more operational parameters, such as to increase ordecrease a rate of agitation and/or inject an additional amount ofcatalyst.

In yet another example of a static agitator mixing monomers in achemical processing plant to produce a polymer, the expert system mayuse feedback related to the viscosity of the polymer, color of thepolymer, reactivity of the polymer and the like to iterate to a newsetting or parameter for the agitator, such as for example, a settingthat alters the Reynolds number, an increase in temperature, a pressureincrease, and the like.

In a further example of a catalytic reactor in a chemical processingplant, the expert system may use feedback related to a reaction rate, aproduct concentration, a product color, and the like. In this example,as feedback is received during operation of the catalytic reactor, theexpert system may instruct the reactor to modify one or more operationalparameters, such as to increase or decrease a temperature and/or injectan additional amount of catalyst.

In yet a further example of a thermic heating systems in a chemicalprocessing or food plant, the expert system may use feedback related toBTUs out of the system, a flow rate, and the like. In this example, asfeedback is received during operation of the thermic heating system, theexpert system may instruct the system to modify one or more operationalparameters, such as to change the input feedstock, to increase the flowof the feedstock, and the like.

In still a further example of using boiler feed water in a refinery, theexpert system may use feedback related to an aeration level, atemperature, and the like. In this example, as feedback is receivedrelated to the boiler feed water, the expert system may instruct thesystem to modify one or more operational parameters of a boiler, such asto increase a reduction in aeration, to increase the flow of the feedwater, and the like.

In still a further example of a storage tank in a refinery, the expertsystem may use feedback related to a temperature, a pressure, a flowrate out of the tank, and the like. In this example, as feedback isreceived related to the storage tank, the expert system may instruct thesystem to modify one or more operational parameters of, such as toincrease cooling or heating begin agitation, and the like.

In an example of a condensate/make-up water system in a power stationthat condenses steam from turbines and recirculates it back to a boilerfeeder along with make-up water, the expert system may use feedbackrelated to measuring inward air leaks, heat transfer, and make-up waterquality. In this example, as feedback is received related to thecondensate/make-up water system, the expert system may instruct thesystem to increase a purification of the make-up water, bring a vacuumpump online, and the like.

In another example of a stirrer in a food plant, the expert system mayuse feedback related to a viscosity of the food, a color of the food, atemperature of the food, and the like. In this example, as feedback isreceived, the expert system may instruct the stirrer to speed up or slowdown, depending on the predicted success in reaching a goal.

In another example of a pressure cooker in a food plant, the expertsystem may use feedback related to a viscosity of the food, a color ofthe food, a temperature of the food, and the like. In this example, asfeedback is received, the expert system may instruct the pressure cookerto continue operating, increase a temperature, or the like, depending onthe predicted success in reaching a goal.

In an embodiment (FIG. 74), a system 11100 for data collection in anindustrial environment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors11102, and a machine learning data analysis circuit 11110 structured toreceive the output data 11108 and learn received output data patterns11112 indicative of an outcome, wherein the machine learning dataanalysis circuit 11110 is structured to learn received output datapatterns 11112 by being seeded with a model 11114 based onindustry-specific feedback 11118. The model 11114 may be a physicalmodel, an operational model, or a system model. The industry-specificfeedback 11118 may be one or more of a utilization measure, anefficiency measure (e.g., power and/or financial), a measure of successin prediction or anticipation of states (e.g., an avoidance andmitigation of faults), a productivity measure (e.g., a workflow), ayield measure, and a profit measure. The industry-specific feedback11118 includes an amount of power generated by a machine about which theinput sensors provide information during operation of the machine. Theindustry-specific feedback 11118 includes a measure of the output of anassembly line about which the input sensors provide information. Theindustry-specific feedback 11118 includes a failure rate of units ofproduct produced by a machine about which the input sensors provideinformation. The industry-specific feedback 11118 includes a fault rateof a machine about which the input sensors provide information. Theindustry-specific feedback 11118 includes the power utilizationefficiency of a machine about which the input sensors provideinformation, wherein the machine is one of a turbine, a transformer, agenerator, a compressor, one that stores energy, and one that includespower train components (e.g., the rate of extraction of a material by amachine about which the input sensors provide information, the rate ofproduction of a gas by a machine about which the input sensors provideinformation, the rate of production of a hydrocarbon product by amachine about which the input sensors provide information), and the rateof production of a chemical product by a machine about which the inputsensors provide information. The machine learning data analysis circuit11110 may be further structured to learn received output data patterns11112 based on the outcome. The system 11100 may keep or modifyoperational parameters or equipment. The controller 11106 may adjust theweighting of the machine learning data analysis circuit 11110 based onthe learned received output data patterns 11112 or the outcome, collectmore/fewer data points from the input sensors based on the learnedreceived output data patterns 11112 or the outcome, change a datastorage technique for the output data 11108 based on the learnedreceived output data patterns 11112 or the outcome, change a datapresentation mode or manner based on the learned received output datapatterns 11112 or the outcome, and apply one or more filters (low pass,high pass, band pass, etc.) to the output data 11108. In embodiments,the system 11100 may remove/re-task under-utilized equipment based onone or more of the learned received output data patterns 11112 and theoutcome. The machine learning data analysis circuit 11110 may include aneural network expert system. The input sensors may measure vibrationand noise data. The machine learning data analysis circuit 11110 may bestructured to learn received output data patterns 11112 indicative ofprogress/alignment with one or more goals/guidelines (e.g., which may bedetermined by a different subset of the input sensors). The machinelearning data analysis circuit 11110 may be structured to learn receivedoutput data patterns 11112 indicative of an unknown variable. Themachine learning data analysis circuit 11110 may be structured to learnreceived output data patterns 11112 indicative of a preferred inputamong available inputs. The machine learning data analysis circuit 11110may be structured to learn received output data patterns 11112indicative of a preferred input data collection band among availableinput data collection bands. The machine learning data analysis circuit11110 may be disposed in part on a machine, on one or more datacollectors, in network infrastructure, in the cloud, or any combinationthereof. The system 11100 may be deployed on the data collection circuit11104. The system 11100 may be distributed between the data collectioncircuit 11104 and a remote infrastructure. The data collection circuit11104 may include a data collector.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a utilization measure.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on an efficiency measure.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a measure of success inprediction or anticipation of states.

In embodiments, a system 11100 for data collection in an industrialenvironment may include a plurality of input sensors 11102communicatively coupled to a controller 11106, a data collection circuit11104 structured to collect output data 11108 from the input sensors,and a machine learning data analysis circuit 11110 structured to receivethe output data 11108 and learn received output data patterns 11112indicative of an outcome, wherein the machine learning data analysiscircuit 11110 is structured to learn received output data patterns 11112by being seeded with a model 11114 based on a productivity measure.

Clause 1. In embodiments, a system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a controller; a data collection circuit structured to collectoutput data from the input sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns indicative of an outcome, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns by being seeded with a model based on industry-specificfeedback. 2. The system of clause 1, wherein the model is a physicalmodel, an operational model, or a system model. 3. The system of clause1, wherein the industry-specific feedback is a utilization measure. 4.The system of clause 1, wherein the industry-specific feedback is anefficiency measure. 5. The system of clause 4, wherein the efficiencymeasure is one of power and financial. 6. The system of clause 1,wherein the industry-specific feedback is a measure of success inprediction or anticipation of states. 7. The system of clause 6, whereinthe measure of success is an avoidance and mitigation of faults. 8. Thesystem of clause 1, wherein the industry-specific feedback is aproductivity measure. 9. The system of clause 8, wherein theproductivity measure is a workflow. 10. The system of clause 1, whereinthe industry-specific feedback is a yield measure. 11. The system ofclause 1, wherein the industry-specific feedback is a profit measure.12. The system of clause 1, wherein the machine learning data analysiscircuit is further structured to learn received output data patternsbased on the outcome. 13. The system of clause 1, wherein the systemkeeps or modifies operational parameters or equipment. 14. The system ofclause 1, wherein the controller adjusts the weighting of the machinelearning data analysis circuit based on the learned received output datapatterns or the outcome. 15. The system of clause 1, wherein thecontroller collects more/fewer data points from the input sensors basedon the learned received output data patterns or the outcome. 16. Thesystem of clause 1, wherein the controller changes a data storagetechnique for the output data based on the learned received output datapatterns or the outcome. 17. The system of clause 1, wherein thecontroller changes a data presentation mode or manner based on thelearned received output data patterns or the outcome. 18. The system ofclause 1, wherein the controller applies one or more filters (low pass,high pass, band pass, etc.) to the output data. 19. The system of clause1, wherein the system removes/re-tasks under-utilized equipment based onone or more of the learned received output data patterns and theoutcome. 20. The system of clause 1, wherein the machine learning dataanalysis circuit comprises a neural network expert system. 21. Thesystem of clause 1, wherein the input sensors measure vibration andnoise data. 22. The system of clause 1, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns indicative of progress/alignment with one or moregoals/guidelines. 23. The system of clause 22, whereinprogress/alignment of each goal/guideline is determined by a differentsubset of the input sensors. 24. The system of clause 1, wherein themachine learning data analysis circuit is structured to learn receivedoutput data patterns indicative of an unknown variable. 25. The systemof clause 1, wherein the machine learning data analysis circuit isstructured to learn received output data patterns indicative of apreferred input among available inputs. 26. The system of clause 1,wherein the machine learning data analysis circuit is structured tolearn received output data patterns indicative of a preferred input datacollection band among available input data collection bands. 27. Thesystem of clause 1, wherein the machine learning data analysis circuitis disposed in part on a machine, on one or more data collectors, innetwork infrastructure, in the cloud, or any combination thereof. 28.The system of clause 1, wherein the system is deployed on the datacollection circuit. 29. The system of clause 1, wherein the system isdistributed between the data collection circuit and a remoteinfrastructure. 30. The system of clause 1, wherein theindustry-specific feedback includes an amount of power generated by amachine about which the input sensors provide information duringoperation of the machine. 31. The system of clause 1, wherein theindustry-specific feedback includes a measure of the output of anassembly line about which the input sensors provide information. 32. Thesystem of clause 1, wherein the industry-specific feedback includes afailure rate of units of product produced by a machine about which theinput sensors provide information. 33. The system of clause 1, whereinthe industry-specific feedback includes a fault rate of a machine aboutwhich the input sensors provide information. 34. The system of clause 1,wherein the industry-specific feedback includes the power utilizationefficiency of a machine about which the input sensors provideinformation. 35. The system of clause 34, wherein the machine is aturbine. 36. The system of clause 34, wherein the machine is atransformer. 37. The system of clause 34, wherein the machine is agenerator. 38. The system of clause 34, wherein the machine is acompressor. 39. The system of clause 34, wherein the machine storesenergy. 40. The system of clause 1, wherein the machine includes powertrain components. 41. The system of clause 34, wherein theindustry-specific feedback includes the rate of extraction of a materialby a machine about which the input sensors provide information. 42. Thesystem of clause 34, wherein the industry-specific feedback includes therate of production of a gas by a machine about which the input sensorsprovide information. 43. The system of clause 34, wherein theindustry-specific feedback includes the rate of production of ahydrocarbon product by a machine about which the input sensors provideinformation. 44. The system of clause 34, wherein the industry-specificfeedback includes the rate of production of a chemical product by amachine about which the input sensors provide information. 45. Thesystem of clause 1, wherein the data collection circuit comprises a datacollector. 46. A system for data collection in an industrialenvironment, comprising: a plurality of input sensors communicativelycoupled to a controller; a data collection circuit structured to collectoutput data from the input sensors; and a machine learning data analysiscircuit structured to receive the output data and learn received outputdata patterns indicative of an outcome, wherein the machine learningdata analysis circuit is structured to learn received output datapatterns by being seeded with a model based on a utilization measure.47. A system for data collection in an industrial environment,comprising: a plurality of input sensors communicatively coupled to acontroller; a data collection circuit structured to collect output datafrom the input sensors; and a machine learning data analysis circuitstructured to receive the output data and learn received output datapatterns indicative of an outcome, wherein the machine learning dataanalysis circuit is structured to learn received output data patterns bybeing seeded with a model based on an efficiency measure. 48. A systemfor data collection in an industrial environment, comprising: aplurality of input sensors communicatively coupled to a controller; adata collection circuit structured to collect output data from the inputsensors; and a machine learning data analysis circuit structured toreceive the output data and learn received output data patternsindicative of an outcome, wherein the machine learning data analysiscircuit is structured to learn received output data patterns by beingseeded with a model based on a measure of success in prediction oranticipation of states. 49. A system for data collection in anindustrial environment, comprising: a plurality of input sensorscommunicatively coupled to a controller; a data collection circuitstructured to collect output data from the input sensors; and a machinelearning data analysis circuit structured to receive the output data andlearn received output data patterns indicative of an outcome, whereinthe machine learning data analysis circuit is structured to learnreceived output data patterns by being seeded with a model based on aproductivity measure.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, set a parameter of a data collection band for collection by adata collector. The parameter may relate to at least one of setting afrequency range for collection and setting an extent of granularity forcollection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, identify a set of sensors among a larger set of availablesensors for collection by a data collector. The user interface mayinclude views of available data collectors, their capabilities, one ormore corresponding smart bands, and the like.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a set of inputs to be multiplexed among a set ofavailable inputs.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, select a component of an industrial machine displayed in thegraphical user interface for data collection, view a set of sensors thatare available to provide data about the industrial machine, and select asubset of sensors for data collection.

In embodiments, a system for data collection in an industrialenvironment may include an expert system graphical user interface inwhich a user may, by interacting with a graphical user interfaceelement, view a set of indicators of fault conditions of one or moreindustrial machines, where the fault conditions are identified byapplication of an expert system to data collected from a set of datacollectors. In embodiments, the fault conditions may be identified bymanufacturers of portions of the one or more industrial machines. Thefault conditions may be identified by analysis of industry trade data,third-party testing agency data, industry standards, and the like. Inembodiments, a set of indicators of fault conditions of one or moreindustrial machines may include indicators of stress, vibration, heat,wear, ultrasonic signature, operational deflection shape, and the like,optionally including any of the widely varying conditions that can besensed by the types of sensors described throughout this disclosure andthe documents incorporated herein by reference.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of component parts of an industrial machinefor establishing smart-band monitoring and in response thereto presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of conditions of an industrial machine forestablishing smart-band monitoring and, in response thereto, presentsthe user with at least one smart-band definition of an acceptable rangeof values for at least one sensor of the industrial machine and a listof correlated sensors from which data will be gathered and analyzed whenan out of acceptable range condition is detected from the at least onesensor.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that enablesa user to select from a list of reliability measures of an industrialmachine for establishing smart-band monitoring and, in response thereto,presents the user with at least one smart-band definition of anacceptable range of values for at least one sensor of the industrialmachine and a list of correlated sensors from which data will begathered and analyzed when an out of acceptable range condition isdetected from the at least one sensor. In the system, the reliabilitymeasures may include one or more of industry average data,manufacturer's specifications, material specifications, recommendations,and the like. In embodiments, reliability measures may include measuresthat correlate to failures, such as stress, vibration, heat, wear,ultrasonic signature, operational deflection shape effect, and the like.In embodiments, manufacturer's specifications may include cycle count,working time, maintenance recommendations, maintenance schedules,operational limits, material limits, warranty terms, and the like. Inembodiments, the sensors in the industrial environment may be correlatedto manufacturer's specifications by associating a condition being sensedby the sensor to a specification type. In embodiments, a non-limitingexample of correlating a sensor to a manufacturer's specification mayinclude a duty cycle specification being correlated to a sensor thatdetects revolutions of a moving part. In embodiments, a temperaturespecification may correlate to a thermal sensor disposed to sense anambient temperature proximal to the industrial machine.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface thatautomatically creates a smart-band group of sensors disposed in theindustrial environment in response to receiving a condition of theindustrial environment for monitoring and an acceptable range of valuesfor the condition.

In embodiments, a system for data collection in an industrialenvironment may include an expert graphical user interface that presentsa representation of components of an industrial machine deployable inthe industrial environment on an electronic display, and in response toa user selecting one or more of the components, searches a database ofindustrial machine failure modes for modes involving the selectedcomponent(s) and conditions associated with the failure mode(s) to bemonitored, and further identifies a plurality of sensors in, on, oravailable to be disposed on the presented machine representation fromwhich data will automatically be captured when the condition(s) to bemonitored are detected to be outside of an acceptable range. Inembodiments, the identified plurality of sensors includes at least onesensor through which the condition(s) will be monitored.

In embodiments, a system for data collection in an industrialenvironment may include a user interface for working with smart bandsthat may facilitate a user identifying sensors to include in a smartband group of sensors by selecting sensors presented on a map of amachine in the environment. A user may be guided to select among asubset of all possible sensors based on smart band criteria, such astypes of sensor data required for the smart band. A smart band may befocused on detecting trending conditions in a portion of the industrialenvironment; therefore, the user interface may direct the user chooseamong an identified subset of sensors, such as by only allowing sensorsproximal to the smart band directed portion of the environment to beselectable in the user interface.

In embodiments, a smart band data collection configuration anddeployment user interface may include views of components in anindustrial environment and related available sensors. In embodiments, inresponse to selection of a component part of an industrial machinedepicted in the user interface, sensors associated with smart band datacollection for the component part may be highlighted so that the usermay select one or more of the sensors. User selection in this contextmay comprise a verification of an automatic selection of sensors, ormanually identifying sensors to include in the smart band sensor group.

In embodiments, in response to selection of a smart band condition, suchas trending of bearing temperature, a user interface for smart bandconfiguration and use may automatically identify and present sensorsthat contribute to smart band analysis for the condition. A user may beresponsive to this presentation of sensors, confirm or otherwiseacknowledge one or more sensors individually or as a set to be includedin the smart band data collection group.

In embodiments, a smart band user interface may present locations ofindustrial machines in an industrial environment on a map. The locationsmay be annotated with indicators of smart band data collection templatesthat are configured for collecting smart band data for the machines atthe annotated locations. The locations may be color coded to reflect adegree of smart band coverage for a machine at the location. Inembodiments, a location of a machine with a high degree of smart bandcoverage may be colored green, whereas a location of a machine with lowsmart band coverage may be colored red or some other contrasting color.Other annotations, such as visual annotations may be used. A user mayselect a machine at a location and by dragging the selected machine to alocation of a second machine, effectively configure smart bands for thesecond machine that correspond to smart bands for the first machine. Inthis way, a user may configure several smart band data collectiontemplates for a newly added machine or a new industrial environment andthe like.

In embodiments, various configurations and selections of smart bands maybe stored for use throughout a data collection platform, such as forselecting templates for sensing, templates for routing, provisioning ofdevices and the like, as well as for direct the placement of sensors,such as by personnel or by machines, such as autonomous orremote-control drones.

In embodiments, a smart band user interface may present a map of anindustrial environment that may include industrial machines,machine-specific data collectors, mobile data collectors (robotic andhuman), and the like. A user may view a list of smart band datacollection actions to be performed and may select a data collectionresource set to undertake the collection. In an example, a guided mobilerobot may be equipped with data collection systems for collecting datafor a plurality of smart band data sets. A user may view an industrialenvironment with which the robot is associated and assign the robot toperform a smart band data collection activity by selecting the robot, asmart band data collection template, and a location in the industrialenvironment, such as a machine or a part of a machine. The userinterface may provide a status of the collection undertaking so that theuser can be informed when the data collection is complete.

In embodiments, a smart band operation management user interface mayinclude presentation of smart band data collection activity, analysis ofresults, actions taken based on results, suggestions for changes tosmart band data collection (e.g., addition of sensors to a smart bandcollection template, increasing duration of data collection for atemplate-specific collection activity), and the like. The user interfacemay facilitate “what if” type analysis by presenting potential impactson reliability, costs, resource utilization, data collection tradeoffs,maintenance schedule impacts, risk of failure (increase/decrease), andthe like in response to a user's attempt to make a change to a smartband data collection template, such as a user relaxing a threshold forperforming smart band data collection and the like. In embodiments, auser may select or enter a target budget for preventive maintenance perunit time (e.g., per month, quarter, and the like) into the userinterface and an expert system of the user interface may recommend asmart band data collection template and thresholds for complying withthe budget.

In embodiments, a smart band user interface may facilitate a userconfiguring a system for data collection in an industrial environmentfor smart band data gathering. The user interface may include display ofindustrial machine components, such as motors, linkages, bearings, andthe like that a user may select. In response to such a selection, anexpert system may work with the user interface to present a list ofpotential failure conditions related to the part to monitor. The usermay select one or more conditions to monitor. The user interface maypresent the conditions to monitor as a set that the user may be asked toapprove. The user may indicate acceptance of the set or of selectconditions in the set monitor. As a follow-on to a userselection/approval of one or more conditions to monitor, the userinterface may display a map of relevant sensors available in theindustrial environment for collecting data as a smart band group ofsensors. The relevant sensors may be associated with one or more parts(e.g., the part(s) originally selected by the user), one or more failureconditions, and the like.

In embodiments, the expert system may compare the relevant sensors inthe environment to a preferred set of sensors for smart band monitoringof the failure condition(s) and provide feedback to the user, such as aconfidence factor for performing smart band monitoring based on theavailable sensors for the failure condition(s). The user may evaluatethe failure condition and smart band analysis information presented andmay take an action in the user interface, such as approving the relevantsensors. In response, a smart band data collection template forconfiguring the data collection system may be created. In embodiments, asmart band data collection template may be created independently of auser approval. In such embodiments, the user may indicate explicitly orimplicitly via approval of the smart band analysis information anapproval of the created template.

In embodiments, a smart band user interface may work with an expertsystem to present candidate portions of an industrial machine in anindustrial environment for smart band condition monitoring based oninformation such as manufacturer's specifications, statisticalinformation derived from real-world experience with similar industrialmachines, and the like. In embodiments, the user interface may permit auser to select certain aspects of the smart band data collection andanalysis process including—for example, a degree of reliability/failurerisk to monitor (e.g., near failure, best performance, industry average,and the like). In response thereto, the expert system may adjust anaspect of the smart band analysis, such as a range of acceptable valueto monitor, a monitor frequency, a data collection frequency, a datacollection amount, a priority for the data collection activity (e.g.,effectively a priority of a template for data collection for the smartband), weightings of data from sensors (e.g., specific sensors in thegroup, types of sensors, and the like).

In embodiments, a smart bands user interface may be structured to allowa user to let an expert system recommend one or more smart bands toimplement based on a range of comparative data that the user mightprioritize, such as industry average data, industry best data, near-bycomparable machines, most similarly configured machines, and the like.Based on the comparative data weighting, the expert system may use theuser interface to recommend one or more smart band templates that alignwith the weighting to the user, who may take an action in the userinterface, such as approving one or more of the recommended templatesfor use.

In embodiments, a user interface for configuring arrangement of sensorsin an industrial environment may include recommendations by industrialenvironment equipment suppliers (e.g., manufacturers, wholesalers,distributors, dealers, third-party consultants, and the like) ofgroup(s) of sensors to include for performing smart band analysis ofcomponents of the industrial equipment. The information may be presentedto a user as data collection template(s) that the user may indicate asbeing accepted/approved, such as by positioning a graphic representing atemplate(s) over a portion of the industrial equipment.

In embodiments, a smart band discovery portal may facilitate sharing ofsmart band related information, such as recommendations, actual usecases, results of smart band data collection and processing, and thelike. The discovery portal may be embodied as a panel in a smart banduser interface.

In embodiments, a smart band assessment portal may facilitate assessmentof smart band-based data collection and analysis. Content that may bepresented in such a portal may include depictions of uses of existingsmart band templates for one or more industrial machines, industrialenvironments, industries, and the like. A value of a smart band may beascribed to each smart band in the portal based, for example, onhistorical use and outcomes. A smart band assessment portal may alsoinclude visualization of candidate sensors to include in a smart banddata collection template based on a range of factors including ascribedvalue, preventive maintenance costs, failure condition being monitored,and the like.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor industrial components, such as of factory-based air conditioningunits. A user interface of a system for data collection for smart bandanalysis of air conditioning units may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific air conditioning system installations. In embodiments, majorcomponents of an air conditioning system, such as a compressor,condenser, heat exchanger, ducting, coolant regulators, filters, fans,and the like along with corresponding sensors for a particularinstallation of the air conditioning system may be depicted in a userinterface. A user may select one or more of these components in the userinterface for configuring a system for smart band data collection. Inresponse to the user selecting, for example, a coolant compressor,sensors associated with the compressor may be automatically identifiedin the user interface. The user may be presented with a recommended datacollection template to perform smart band data collection for theselected compressor. Alternatively, the user may request a candidatecollection template from a community of smart band users, such asthrough a smart band template sharing panel of the user interface. Oncea template is selected, the user interface may offer the usercustomization options, such as frequency of collection, degree ofreliability to monitor, and the like. Upon final acceptance of thetemplate, the user interface may interact with a data collection systemof the installed air conditioning system (if such a system is available)to implement the data collection template and provide an indication tothe user of the result of implementing the template. In responsethereto, the user may make a final approval of the template for use withthe air conditioning unit.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor oil and gas refinery-based chillers. A user interface of a systemfor data collection for smart band analysis of refinery-based chillersmay facilitate graphical configuration of smart band data collectiontemplates and the like for specific refinery-based chillerinstallations. In embodiments, major components of a refinery-basedchiller including heat exchangers, compressors, water regulators and thelike along with corresponding sensors for the particular installation ofthe refinery-based chiller may be depicted in a user interface. A usermay select one or more of these components in the user interface forconfiguring a system for smart band data collection. In response to theuser selecting, for example, water regulators, sensors associated withthe water regulators may be automatically identified in the userinterface. The user may be presented with a recommended data collectiontemplate to perform smart band data collection for the selectedcomponent. Alternatively, the user may request a candidate collectiontemplate from a community of smart band users, such as through a smartband template sharing panel of the user interface. Once a template isselected, the user interface may offer the user customization options,such as frequency of collection, degree of reliability to monitor, andthe like. Upon final acceptance of the template, the user interface mayinteract with a data collection system of the installed refinery-basedchiller (if such a system is available) to implement the data collectiontemplate and provide an indication to the user of the result ofimplementing the template. In response thereto, the user may make afinal approval of the template for use with the refinery-based chiller.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor automotive production line robotic assembly systems. A userinterface of a system for data collection for smart band analysis ofproduction line robotic assembly systems may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific production line robotic assembly system installations. Inembodiments, major components of a production line robotic assemblysystem including motors, linkages, tool handlers, positioning systemsand the like along with corresponding sensors for the particularinstallation of the production line robotic assembly system may bedepicted in a user interface. A user may select one or more of thesecomponents in the user interface for configuring a system for smart banddata collection. In response to the user selecting, for example, roboticlinkage sensors associated with the robotic linkages may beautomatically identified in the user interface. The user may bepresented with a recommended data collection template to perform smartband data collection for the selected component. Alternatively, the usermay request a candidate collection template from a community of smartband users, such as through a smart band template sharing panel of theuser interface. Once a template is selected, the user interface mayoffer the user customization options, such as frequency of collection,degree of reliability to monitor, and the like. Upon final acceptance ofthe template, the user interface may interact with a data collectionsystem of the installed production line robotic assembly system (if sucha system is available) to implement the data collection template andprovide an indication to the user of the result of implementing thetemplate. In response thereto, the user may make a final approval of thetemplate for use with the production line robotic assembly system.

In embodiments, a smart bands graphical user interface associated with asystem for data collection in an industrial environment may be deployedfor automotive production line robotic assembly systems. A userinterface of a system for data collection for smart band analysis ofproduction line robotic assembly systems may facilitate graphicalconfiguration of smart band data collection templates and the like forspecific production line robotic assembly system installations. Inembodiments, major components of construction site boring machinery,such as the cutter head, which itself is a subsystem that may have manycomponents, control systems, debris handling and conveying components,precast concrete delivery and installation subsystems and the like alongwith corresponding sensors for the particular installation of theproduction line robotic assembly system may be depicted in a userinterface. A user may select one or more of these components in the userinterface for configuring a system for smart band data collection. Inresponse to the user selecting, for example, debris handling componentssensors associated with the debris handling components, such as aconveyer may be automatically identified in the user interface. The usermay be presented with a recommended data collection template to performsmart band data collection for the selected component. Alternatively,the user may request a candidate collection template from a community ofsmart band users, such as through a smart band template sharing panel ofthe user interface. Once a template is selected, the user interface mayoffer the user customization options, such as frequency of collection,degree of reliability to monitor, and the like. Upon final acceptance ofthe template, the user interface may interact with a data collectionsystem of the installed production line robotic assembly system (if sucha system is available) to implement the data collection template andprovide an indication to the user of the result of implementing thetemplate. In response thereto, the user may make a final approval of thetemplate for use with the production line robotic assembly system.

Referring to FIG. 75, an exemplary user interface for smart bandconfiguration of a system for data collection in an industrialenvironment is depicted. The user interface 11200 may include anindustrial environment visualization portion 11202 in which may bedepicted one or more sensors, machines, and the like. Each sensor,machine, or portion thereof (e.g., motor, compressor, and the like) maybe selectable as part of a smart-band configuration process. Likewise,each sensor, machine or portion thereof may be visually highlightedduring the smart-band configuration process, such as in response to userselection, or automated identification as being part of a group of smartband sensors. The user interface may also include a smart band selectionportion 11204 or panel in which smart band indicators, failure modes,and the like may be depicted in selectable elements. User selection of asymptom, failure mode and the like may cause corresponding components,sensors, machines, and the like in the industrial visualization portionto be highlighted. The user interface may also include a customizationpanel 11206 in which attributes of a selected smart band, such asacceptable ranges, frequency of monitoring and the like may be madeavailable for a user to adjust.

Cause 1. In embodiments, a system comprising: a user interfacecomprising: a selectable graphical element that facilitates selection ofa representation of a component of an industrial machine from anindustrial environment in which a plurality of sensors is deployed fromwhich a data collection system collects data for the system for whichthe user interface enables interaction; and selectable graphicalelements representing a portion of the plurality of sensors thatfacilitate selection of a sensors to form a data collection subset ofsensors in the industrial environment. 2. The system of clause 1,wherein selection of sensors to form a data collection subset results ina data collection template adapted to facilitate configuring the datarouting and collection system for collecting data from the datacollection subset of sensors. 3. The system of clause 1, wherein theuser interface comprises an expert system that analyzes a user selectionof a graphical element that facilitates selection of a component andadjusts the selectable graphical elements representing a portion of theplurality of sensors to activate only sensors associated with acomponent associated with the selected graphical element. 4. The systemof clause 1, wherein the selectable graphical element that facilitatesselection of a component of an industrial machine further facilitatespresentation of a plurality of data collection templates associated withthe component. 5. The system of clause 1, wherein the portion of theplurality of sensors comprises a smart band group of sensors. 6. Thesystem of clause 5, wherein the smart band group of sensors comprisessensors for a component of the industrial machine selected by theselectable graphical element. 7. A system comprising: an expertgraphical user interface comprising representations of a plurality ofcomponents of an industrial machine from an industrial environment inwhich a plurality of sensors is deployed from which a data collectionsystem collects data for the system for which the user interface enablesinteraction, wherein at least one representation of the plurality ofcomponents is selectable by a user in the user interface; a database ofindustrial machine failure modes; and a database searching facility thatsearches the database of failure modes for modes that correspond to auser selection of a component of the plurality of components. 8. Thesystem of clause 7, comprising a database of conditions associated withthe failure modes. 9. The system of clause 8, wherein the database ofconditions includes a list of sensors in the industrial environmentassociated with the condition. 10. The system of clause 9, wherein thedatabase searching facility further searches the database of conditionsfor sensors that correspond to at least one condition and indicates thesensors in the graphical user interface. 11. The system of clause 7,wherein the user selection of a component of the plurality of componentscauses a data collection template for configuring the data routing andcollection system to automatically collect data from sensors associatedwith the selected component. 12. A method comprising: presenting in anexpert graphical user interface a list of reliability measures of anindustrial machine; facilitating user selection of one reliabilitymeasure from the list; presenting a representation of a smart band datacollection template associated with the selected reliability measure;and in response to a user indication of acceptance of the smart banddata collection template, configuring a data routing and collectionsystem to collect data from a plurality of sensors in an industrialenvironment in response to a data value from one of the plurality ofsensors being detected outside of an acceptable range of data values.13. The method of clause 12, wherein the reliability measures includeone or more of industry average data, manufacturer's specifications,manufacturer's material specifications, and manufacturer'srecommendations. 14. The method of clause 13, wherein include themanufacturer's specifications include at least one of cycle count,working time, maintenance recommendations, maintenance schedules,operational limits, material limits, and warranty terms. 15. The methodof clause 12, wherein the reliability measures correlate to failuresselected from the list consisting of stress, vibration, heat, wear,ultrasonic signature, and operational deflection shape effect. 16. Themethod of clause 12, further comprising correlating sensors in theindustrial environment to manufacturer's specifications. 17. The methodof clause 16, wherein correlating comprises matching a duty cyclespecification to a sensor that detects revolutions of a moving part. 18.The method of clause 16, wherein correlating comprises matching atemperature specification with a thermal sensor disposed to sense anambient temperature proximal to the industrial machine. 19. The methodof clause 16, further comprising dynamically setting the acceptablerange of data values based on a result of the correlating. 20. Themethod of clause 16, further comprising automatically determining theone of the plurality of sensors for detecting the data value outside ofthe acceptable range based on a result of the correlating.

Back calculation, such as for determining possible root causes offailures and the like, may benefit from a graphical approach thatfacilitates visualizing an industrial environment, machine, or portionthereof marked with indications of information sources that may providedata such as sensors and the like related to the failure. A failed part,such as a bearing, may be associated with other parts, such as shaft,motor, and the like. Sensors for monitoring conditions of the bearingand the associated parts may provide information that could indicate apotential source of failure. Such information may also be useful tosuggest indicators, such as changes in sensor output, to monitor oravoid the failure in the future. A system that facilitates a graphicalapproach for back-calculation may interact with sensor data collectionand analysis systems to at least partially automate aspects related todata collection and processing determined from a back-calculationprocess.

In embodiments, a system for data collection in an industrialenvironment may include a user interface in which portions of anindustrial machine associated with a condition of interest, such as afailure condition, are presented on an electronic display along withsensor data types contributing to the condition of interest, datacollection points (e.g., sensors) associated with the machine portionsthat monitor the data types, a set of data from the data collectionpoints that was collected and used to determine the condition ofinterest, and an annotation of sensors that delivered exceptional data,such as data that is out of an acceptable range, and the like, that mayhave been used to determine the condition of interest. The userinterface may access a description of the machine that facilitatesdetermining and visualizing related components, such as bearing, shafts,brakes, rotors, motor housings, and the like that contribute to afunction, such as rotating a turbine. The user interface may also accessa data set that relates sensors disposed in and about the machine withthe components. Information in the data set may include descriptions ofthe sensors, their function, a condition that each senses, typical oracceptable ranges of values output from the sensors, and the like. Theinformation in the data set may also identify a plurality of potentialpathways in a system for data collection in an industrial environmentfor sensor data to be delivered to a data collector. The user interfacemay also access a data set that may include data collection templatesused to configure a data collection system for collecting data from thesensors to meet specific purposes (e.g., to collect data from groups ofsensors into a sensor data set suitable for determining a condition ofthe machine, such as a degree of slippage of the shaft relative to themotor, and the like).

In embodiments, a method of back-calculation for determining candidatesources of data collection for data that contributes to a condition ofan industrial machine may include following routes of data collectiondetermined from a configuration and operational template of a datacollection system for collecting data from sensors deployed in theindustrial machine that was in place when the contributing data wascollected. A configuration and operational template may describe signalpath switching, multiplexing, collection timing, and the like for datafrom a group of sensors. The group of sensors may be local to acomponent, such as a bearing, or more regionally distributed, such assensors that capture information about the bearing and its relatedcomponents. In embodiments, a data collection template may be configuredfor collecting and processing data to detect a particular condition ofthe industrial machine. Therefore, templates may be correlated toconditions so that performing back-calculation of a condition ofinterest can be guided by the correlated template. Data collected basedon the template may be examined and compared to acceptable ranges ofdata for various sensors. Data that is outside of an acceptable rangemay indicate potential root causes of an unacceptable condition. Inembodiments, a suspect source of data collection may be determined fromthe candidate sources of data collection based on a comparison of datacollected from the candidate data sources with an acceptable range ofdata collected from each candidate data source. Visualizing theseback-calculation based signal paths, candidate sensors, and suspect datasources provides a user with valuable insights into possible root causesof failures and the like.

In embodiments, a method for back-calculation may include visualizingroute(s) of data that contribute to a fault condition detected in anindustrial environment by applying back-calculation to determine sourcesof the contributed data with the visualizing appearing as highlighteddata paths in a visual representation of the data collection system inthe industrial machine. In embodiments, determining sources of data maybe based on a data collection and processing template for the faultcondition. The template may include a configuration of a data collectionsystem when data from the determined sources was collected with thesystem.

When failures occur, or conditions of a portion of a machine in anindustrial environment reach a critical point prior to failure, such asmay be detected during preventive maintenance and the like,back-calculation may be useful in determining information to gather thatmight help avoid the failure and/or improve system performance—forexample, by avoiding substantive degradation in component operation.Visualizing data collection sources, components related to a condition,algorithms that may determine the potential onset of the condition andthe like may facilitate preparation of data collection templates forconfiguring data sensing, routing, and collection resources in a systemfor data collection in an industrial environment. In embodiments,configuring a data collection template for a system for collecting datain an industrial environment may be based on back-calculations appliedto machine failures that identify candidate conditions to monitor foravoiding the machine failures. The resulting template may identifysensors to monitor, sensor data collection path configuration,frequency, and amount of data to collect, acceptable levels of sensordata, and the like. With access to information about the machine, suchas which parts closely relate to others and sensors that collected datafrom parts in the machine, a data collection system configurationtemplate may be automatically generated when a target component isidentified.

In embodiments, a user interface may include a graphical display of datasources as a logical arrangement of sensors that may contribute data toa calculation of a condition of a machine in an industrial environment.A logical arrangement may be based on sensor type, data collectiontemplate, condition, algorithm for determining a condition, and thelike. In an example, a user may wish to view all temperature sensorsthat may contribute to a condition, such as a failure of a part in anindustrial environment. A user interface may communicate with a databaseof machine related information, such as parts that relate to acondition, sensors for those parts, and types of those sensors todetermine the subset of sensors that measure temperature. The userinterface may highlight those sensors. The user interface may activateselectable graphical elements for those sensors that, when selected bythe user, may present data associated with those sensors, such as sensortype, ranges of data collected, acceptable ranges, actual data valuescollected for a given condition, and the like, such as in a pop-up panelor the like. Similar functionality of the user interface may apply tophysical arrangements of sensors, such as all sensors associated with amotor, boring machine cutting head, wind turbine, and the like.

In embodiments, third-parties, such as component manufacturers, remotemaintenance organizations and the like may benefit from access toback-calculation visualization. Permitting third parties to have accessto back-calculation information, such as sensors that contributedunacceptable data values to a calculation of a condition, visualizationof sensor positioning, and the like may be an option that a user canexercise in a user interface for a graphical approach toback-calculations as described herein. A list of manufacturers ofmachines, sub-systems, individual components, sensors, data collectionsystems, and the like may be presented along with remote maintenanceorganizations, and the like in a portion of a user interface. A user ofthe interface may select one or more of these third-parties to grantaccess to at least a portion of the available data and visualizations.Selecting one or more of these third-parties may also presentstatistical information about the party, such occurrences and frequencyof access to data to which the party is granted access, request from theparty for access, and the like.

In embodiments, visualization of back-calculation analysis may becombined with machine learning so that back-calculations and theirvisualizations may be used to learn potential new diagnoses forconditions, such as failure conditions, to learn new conditions tomonitor, and the like. A user may interact with the user interface toprovide the machine learning techniques feedback to improve results,such as indicating a success or failure of an attempt to preventfailures through specific data collection and processing solutions(e.g., templates), and the like.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to concrete pouring equipment in a construction siteapplication. Concrete pouring equipment may comprise several activecomponents including mixers that may include water and aggregate supplysystems, mixing control systems, mixing motors, directional controllers,concrete sensors and the like, concrete pumps, delivery systems, flowcontrol as well as on/off controls, and the like. Back-calculation offailure or other conditions of active or passive components of aconcrete pouring equipment may benefit from visualization of theequipment, its components, sensors, and other points where data iscollected (e.g., controllers and the like). Visualizing data/conditionscollected from sensors associated with concrete pumps and the like whenperforming back-calculation of a flow rate failure condition may informthe user of a conditions of the pump that may contribute to the flowrate failure. Flow rate may decrease contemporaneously with an increasein temperature of the pump. This may be visualized by, for example,presenting the flow rate sensor data and the pump temperature sensordata in the user interface. This correlation may be noted by an expertsystem or by a user observing the visualization and corrective actionmay be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to digging and extraction systems in a miningapplication. Digging and extraction systems may comprise several activesub-systems including cutting heads, pneumatic drills, jack hammers,excavators, transport systems, and the like. Back-calculation of failureor other conditions of active or passive components of digging andextraction systems may benefit from visualization of the equipment, itscomponents, sensors, and other points where data is collected (e.g.,controllers and the like). Visualizing data/conditions collected fromsensors associated with pneumatic drills and the like when performingback-calculation of a pneumatic line failure condition may inform theuser of a conditions of the drill that may contribute to the linefailure. Line pressure may increase contemporaneously with a change of acondition of the drill. This may be visualized by, for example,presenting the line pressure sensor data and data from sensorsassociated with the drill in the user interface. This correlation may benoted by an expert system or by a user observing the visualization andcorrective action may be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to cooling towers in an oil and gas productionenvironment. Cooling towers may comprise several active componentsincluding feedwater systems, pumps, valves, temperature-controlledoperation, storage systems, mixing systems, and the like.Back-calculation of failure or other conditions of active or passivecomponents of cooling towers may benefit from visualization of theequipment, its components, sensors and other points where data iscollected (e.g., controllers and the like). Visualizing data/conditionscollected from sensors associated with the cooling towers and the likewhen performing back-calculation of a circulation pump failure conditionmay inform the user of a conditions of the cooling towers that maycontribute to the pump failure. Temperature of the feedwater mayincrease contemporaneously with a decrease in output of the circulationpump. This may be visualized by, for example, presenting the feed watertemperature sensor data and the pump output rate sensor data in the userinterface. This correlation may be noted by an expert system or by auser observing the visualization and corrective action may be taken.

In embodiments, methods and systems of back-calculation of datacollected with a system for data collection in an industrial environmentmay be applied to circulation water systems in a power generationapplication. Circulation water systems may comprise several activecomponents including, pumps, storage systems, water coolers, and thelike. Back-calculation of failure or other conditions of active orpassive components of circulation water systems may benefit fromvisualization of the equipment, its components, sensors and other pointswhere data is collected (e.g., controllers and the like). Visualizingdata/conditions collected from sensors associated with water coolers andthe like when performing back-calculation of a circulation watertemperature failure condition may inform the user of a conditions of thecooler that may contribute to the temperature condition failure.Circulation temperature may increase contemporaneously with an increaseof core water cooler temperature. This may be visualized by, forexample, presenting the circulation water temperature sensor data andthe water cooler temperature sensor data in the user interface. Thiscorrelation may be noted by an expert system or by a user observing thevisualization and corrective action may be taken.

Referring to FIG. 76 a graphical approach 11300 for back-calculation isdepicted. Components of an industrial environment may be depicted in amap of the environment 11302. Components that may have a history offailure (with this installation or others) may be highlighted. Inresponse to a selection of one of these components (such as by a usermaking the selection), related components and sensors for the selectedpart and related components may be highlighted, including signal routingpaths for the data from their relevant sensors to a data collector.Additional highlighting may be added to sensors from which unacceptabledata has been collected, thereby indicating potential root causes of afailure of the selected part. The relationships among the parts may bebased at least in part on machine configuration metadata. Therelationship between specific sensors and the failure condition may bebased at least in part on a data collection template associated with thepart and/or associated with the failure condition.

Clause 1. In embodiments, a system comprising: a user interface of asystem adapted to collect data in an industrial environment; the userinterface comprising: a plurality of graphical elements representingmechanical portions of an industrial machine, wherein the plurality ofgraphical elements is associated with a condition of interest generatedby a processor executing a data analysis algorithm; a plurality ofgraphical elements representing data collectors in a system adapted forcollecting data in an industrial environment that collected data used inthe data analysis algorithm; and a plurality of graphical elementsrepresenting sensors used to capture the data used in the data analysisalgorithm, wherein graphical elements for sensors that provided datathat was outside of an acceptable range of data values are indicatedthrough a visual highlight in the user interface. 2. The system ofclause 1, wherein the condition of interest is selected from a list ofconditions of interest presented in the user interface. 3. The system ofclause 1, wherein the condition of interest is a mechanical failure ofat least one of the mechanical portions of the industrial machine. 4.The system of clause 1, wherein the mechanical portions comprise atleast one of a bearing, shaft, rotor, housing, and linkage of theindustrial machine. 5. The system of clause 1, wherein the acceptablerange of data values is available for each sensor. 6. The system ofclause 1, further comprising highlighting data collectors that collectedthe data that was outside of the acceptable range of data values. 7. Thesystem of clause 1, further comprising a data collection systemconfiguration template that facilitates configuring the data collectionsystem to collect the data for calculating the condition of interest. 8.A method of determining candidate sources of a condition of interestcomprising: identifying a data collection template for configuring datarouting and collection resources in a system adapted to collect data inan industrial environment, wherein the template was used to collect datathat contributed to a calculation of the condition of interest;determining paths from data collectors for the collected data to sensorsthat produced the collected data by analyzing the data collectiontemplate; comparing data collected by the sensors with acceptable rangesof data values for data collected by the sensors; and highlighting, inan electronic user interface that depicts the industrial environment andat least one of the sensors, at least one sensor that produced data thatcontributed to the calculation of the condition of interest that isoutside of the acceptable range of data for that sensor. 9. The methodof clause 8, wherein the condition of interest is a failure condition.10. The method of clause 8, wherein the data collection templatecomprises configuration information for at least one of an analogcrosspoint switch, a multiplexer, a hierarchical multiplexer, a sensor,a collector, and a data storage facility of the system adapted tocollect data in the industrial environment. 11. The method of clause 8,wherein the highlighting in the industrial environment compriseshighlighting he at least one sensor, and at least one route of data fromthe sensor to a data collector of the system for data collection in theindustrial environment. 12. The method of clause 8, wherein comparingdata collected by the sensors with acceptable ranges of data valuescomprises comparing data collected by each sensor with an acceptablerange of data values that is specific to each sensor. 13. The method ofclause 8, wherein the calculation of the condition of interest comprisescalculating a trend of data from at least one sensor. 14. The method ofclause 8, wherein the acceptable range of values comprises a trend ofdata values. 15. A method of visualizing routes of data that contributeto a condition of interest that is detected in an industrialenvironment, the method comprising: applying back calculation to thecondition of interest to determine a data collection systemconfiguration template associated with the condition of interest;analyzing the template to determine a configuration of the datacollection system for collecting data for detecting the condition ofinterest; presenting, in an electronic user interface, a map of the datacollection configured by the template; and highlighting, in theelectronic user interface, routes in the data collection system thatreflect paths of data from at least one sensor to at least one datacollector for data that contributes to calculating the condition ofinterest. 16. The method of clause 15 wherein the data collection systemconfiguration template comprises configuration information for at leastone resource deployed in the data collection system selected from thelist consisting of an analog crosspoint switch, a multiplexer, ahierarchical multiplexer, a data collector, and a sensor. 17. The methodof clause 15, further comprising generating a target diagnosis for thecondition of interest by applying machine learning to the backcalculation. 18. The method of clause 15, further comprisinghighlighting in the electronic user interface, sensors that produce dataused in calculating the condition of interest that is outside of anacceptable range of data values for the sensor. 19. The method of clause15, wherein the condition of interest is selected from a list ofconditions of interest presented in the user interface. 20. The systemof clause 15, wherein the condition of interest is a mechanical failureof at least one mechanical portion of the industrial environment. 21.The system of clause 15, wherein the mechanical portions comprise atleast one of a bearing, shaft, rotor, housing, and linkage of theindustrial environment.

In embodiments, a system for data collection in an industrialenvironment may route data from a plurality of sensors in the industrialenvironment to wearable haptic stimulators that present the data fromthe sensors as human detectable stimuli including at least one oftactile, vibration, heat, sound, and force. In embodiments, the hapticstimulus represents an effect on the machine resulting from the senseddata. In embodiments, a bending effect may be presented as bending afinger of a haptic glove. In embodiments, a vibrating effect may bepresented as vibrating a haptic arm band. In embodiments, a heatingeffect may be presented as an increase in temperature of a haptic wristband. In embodiments, an electrical effect (e.g., over voltage, current,and others) may be presented as a change in sound of a phatic audiosystem.

In embodiments, an industrial machine operator haptic user interface maybe adapted to provide haptic stimuli to the operator that is responsiveto the operator's control of the machine, wherein the stimuli indicatean impact on the machine as a result of the operator's control andinteraction with objects in the environment as a result thereof. Inembodiments, sensed conditions of the machine that exceed an acceptablerange may be presented to the operator through the haptic userinterface. In embodiments, the sensed conditions of the machine that arewithin an acceptable range may not be presented to the operator throughthe haptic user interface. In embodiments, the sensed conditions of themachine that are within an acceptable range may presented as naturallanguage representations of confirmation of the operator control. Inembodiments, at least a portion of the haptic user interface is worn bythe operator. In embodiments, a wearable haptic user interface devicemay include force exerting devices along the outer legs of a deviceoperator's uniform. When a vehicle that the operator is controllingapproaches an obstacle along a lateral side of the vehicle, aninflatable bellows may be inflated, exerting pressure against the leg ofthe operator closest to the side of the vehicle approaching theobstacle. The bellows may continue to be inflated, thereby exertingadditional pressure on the operator's leg that is consistent with theproximity of the obstacle. The pressure may be pulsed when contact withthe obstacle is imminent. In another example, an arm band of an operatormay vibrate in coordination with vibration being experienced by aportion of the vehicle that the operator is controlling. These aremerely examples and not intended to be limiting or restrictive of theways in which a wearable haptic feedback user device may be controlledto indicate conditions that are sensed by a system for data collectionin an industrial environment.

In embodiments, a haptic user interface safety system worn by a user inan industrial environment may be adapted to indicate proximity to theuser of equipment in the environment by stimulating a portion of theuser with at least one of pressure, heat, impact, electrical stimuli andthe like, the portion of the user being stimulated may be closest to theequipment. In embodiments, at least one of the type, strength, duration,and frequency of the stimuli is indicative of a risk of injury to theuser.

In embodiments, a wearable haptic user interface device, that may beworn by a user in an industrial environment, may broadcast its locationand related information upon detection of an alert condition in theindustrial environment. The alert condition may be proximal to the userwearing the device, or not proximal but related to the user wearing thedevice. A user may be an emergency responder, so the detection of asituation requiring an emergency responded, the user's haptic device maybroadcast the user's location to facilitate rapid access to the user orby the user to the emergency location. In embodiments, an alertcondition may be determined from monitoring industrial machine sensorsmay be presented to the user as haptic stimuli, with the severity of thealert corresponding to a degree of stimuli. In embodiments, the degreeof stimuli may be based on the severity of the alert, the correspondingstimuli may continue, be repeated, or escalate, optionally includingactivating multiple stimuli concurrently, send alerts to additionalhaptic users, and the like until an acceptable response is detected,e.g., through the haptic UI. The wearable haptic user device may beadapted to communicate with other haptic user devices to facilitatedetecting the acceptable response.

In embodiments, a wearable haptic user interface for use in anindustrial environment may include gloves, rings, wrist bands, watches,arm bands, head gear, belts, necklaces, shirts (e.g., uniform shirt),footwear, pants, ear protectors, safety glasses, vests, overalls,coveralls, and any other article of clothing or accessory that can beadapted to provide haptic stimuli.

In embodiments, wearable haptic device stimuli may be correlated to asensor in an industrial environment. Non-limiting examples include avibration of a wearable haptic device in response to vibration detectedin an industrial environment; increasing or decreasing the temperatureof a wearable haptic device in response to a detected temperature in anindustrial environment; producing sound that changes in pitchresponsively to changes in a sensed electrical signal, and the like. Inembodiments, a severity of wearable haptic device stimuli may correlateto an aspect of a sensed condition in the industrial environment.Non-limiting examples include moderate or short-term vibration for a lowdegree of sensed vibration; strong or long-term vibration stimulationfor an increase in sensed vibration; aggressive, pulsed, and/ormulti-mode stimulation for a high amount of sensed vibration. Wearablehaptic device stimuli may also include lighting (e.g., flashing, colorchanges, and the like), sound, odor, tactile output, motion of thehaptic device (e.g., inflating/deflating a balloon, extension/retractionof an articulated segment, and the like), force/impact, and the like.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from fuel handling systems in a power generationapplication to the user via haptic stimulation. Fuel handling for powergeneration may include solid fuels, such as woodchips, stumps, forestresidue, sticks, energy willow, peat, pellets, bark, straw, agrobiomass, coal, and solid recovery fuel. Handling systems may includereceiving stations that may also sample the fuel, preparation stationsthat may crush or chip wood-based fuel or shred waste-based fuel. Fuelhandling systems may include storage and conveying systems, feed and ashremoval systems and the like. Wearable haptic user interface devices maybe used with fuel handling systems by providing an operator feedback onconditions in the handling environment that the user is otherwiseisolated from. Sensors may detect operational aspects of a solid fuelfeed screw system. Conditions like screw rotational rate, weight of thefuel, type of fuel, and the like may be converted into haptic stimuli toa user while allowing the user to use his hands and provide hisattention to operate the fuel feed system.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from suspension systems of a truck and/or vehicleapplication to the user via haptic stimulation. Haptic simulation may becorrelated with conditions being sensed by the vehicle suspensionsystem. In embodiments, road roughness may be detected and convertedinto vibration-like stimuli of a wearable haptic arm band. Inembodiments, suspension forces (contraction and rebound) may beconverted into stimuli that present a scaled down version of the forcesto the user through a wearable haptic vest.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from hydroponic systems in an agricultureapplication to the user via haptic stimulation. In embodiments, sensorsin hydroponic systems, such as temperature, humidity, water level, plantsize, carbon dioxide/oxygen levels, and the like may be converted towearable device haptic stimuli. As an operator wearing haptic feedbackclothing walks through a hydroponic agriculture facility, sensorsproximal to the operator may signal to the haptic feedback clothingrelevant information, such as temperature or a measure of actualtemperature versus desired temperature that the haptic clothing mayconvert into haptic stimuli. In an example, a wrist band may include athermal stimulator that can change temperature quickly to tracktemperature data or a derivative thereof from sensors in the agricultureenvironment. As a user walks through the facility, the haptic feedbackwristband may change temperature to indicate a degree to which proximaltemperatures are complying with expected temperatures.

In embodiments, a system for data collection in an industrialenvironment may interface with wearable haptic feedback user devices torelay data collected from robotic positioning systems in an automatedproduction line application to the user via haptic stimulation. Hapticfeedback may include receiving a positioning system indicator ofaccuracy and converting it to an audible signal when the accuracy isacceptable, and another type of stimuli when the accuracy is notacceptable.

Referring to FIG. 77, a wearable haptic user interface device forproviding haptic stimuli to a user that is responsive to data collectedin an industrial environment by a system adapted to collect data in theindustrial environment is depicted. A system for data collection 11402in an industrial environment 11400 may include a plurality of sensors.Data from those sensors may be collected and analyzed by a computingsystem. A result of the analysis may be communicated wirelessly to oneor more wearable haptic feedback stimulators 11404 worn by a userassociated with the industrial environment. The wearable haptic feedbackstimulators may interpret the result, convert it into a form of stimulibased on a haptic stimuli-to-sensed condition mapping, and produce thestimuli.

Clause 1. In embodiments, a system for data collection in an industrialenvironment, comprising: a plurality of wearable haptic stimulators thatproduce stimuli selected from the list of stimuli consisting of tactile,vibration, heat, sound, force, odor, and motion; a plurality of sensorsdeployed in the industrial environment to sense conditions in theenvironment; a processor logically disposed between the plurality ofsensors and the wearable haptic stimulators, the processor receivingdata from the sensors representative of the sensed condition,determining at least one haptic stimulation that corresponds to thereceived data, and sending at least one signal for instructing thewearable haptic stimulators to produce the at least one stimulation. 2.The system of clause 1, wherein the haptic stimulation represents aneffect on a machine in the industrial environment resulting from thecondition. 3. The system of clause 2, wherein a bending effect ispresented as bending a haptic device. 4. The system of clause 2, whereina vibrating effect is presented as vibrating a haptic device. 5. Thesystem of clause 2, wherein a heating effect is presented as an increasein temperature of a haptic device. 6. The system of clause 2, wherein anelectrical effect is presented as a change in sound produced by a hapticdevice. 7. The system of clause 2, wherein at least one of the pluralityof wearable haptic stimulators are selected from the list consisting ofa glove, ring, wrist band, wristwatch, arm band, head gear, belt,necklace, shirt, footwear, pants, overalls, coveralls, and safetygoggles. 8. The system of clause 2, wherein the at least one signalcomprises an alert of a condition of interest in the industrialenvironment. 9. The system of clause 8, wherein the at least onestimulation produced in response to the alert signal is repeated by atleast one of the plurality of wearable haptic stimulators until anacceptable response is detected. 10. An industrial machine operatorhaptic user interface that is adapted to provide the operator hapticstimuli responsive to the operator's control of the machine based on atleast one sensed condition of the machine that indicates an impact onthe machine as a result of the operator's control and interaction withobjects in the environment as a result thereof. 11. The user interfaceof clause 10, wherein a sensed condition of the machine that exceeds anacceptable range of data values for the condition is presented to theoperator through the haptic user interface. 12. The user interface ofclause 10, wherein a sensed condition of the machine that is within anacceptable range of data values for the condition is presented asnatural language representations of confirmation of the operator controlvia an audio haptic stimulator. 13. The user interface of clause 10,wherein at least a portion of the haptic user interface is worn by theoperator. 14. The system of clause 10, wherein a vibrating sensedcondition is presented as vibrating stimulation by the haptic userinterface. 15. The system of clause 10, wherein a temperature-basedsensed condition is presented as heat stimulation by the haptic userinterface. 16. A haptic user interface safety system worn by a user inan industrial environment, wherein the interface is adapted to indicateproximity to the user of equipment in the environment by hapticstimulation via a portion of the haptic user interface that is closestto the equipment, wherein at least one of the type, strength, duration,and frequency of the stimulation is indicative of a risk of injury tothe user. 17. The haptic user interface of clause 16, wherein the hapticstimulation is selected from a list consisting of pressure, heat,impact, and electrical stimulation. 18. The haptic user interface ofclause 16 wherein the haptic user interface further comprises a wirelesstransmitter that broadcasts a location of the user. 19. The haptic userinterface of clause 18, wherein the wireless transmitter broadcasts alocation of the user in response to indicating proximity of the user tothe equipment. 20. The haptic user interface of clause 16, wherein theproximity to the user of equipment in the environment is based on sensordata provided to the haptic user interface from a system adapted tocollect data in an industrial environment, wherein the system is adaptedbased on a data collection template associated with a user safetycondition in the industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting a graphical element indicative ofindustrial machine sensed data on an augmented reality (AR) display. Thegraphical element may be adapted to represent a position of the senseddata on a scale of acceptable values of the sensed data. The graphicalelement may be positioned proximal to a sensor detected in the field ofview being augmented that captured the sensed data in the AR display.The graphical element may be a color and the scale may be a color scaleranging from cool colors (e.g., greens, blues) to hot colors (e.g.,yellow, red) and the like. Cool colors may represent data values closerto the midpoint of the acceptable range and the hot colors representingdata values close to or outside of a maximum or minimum value of therange.

In embodiments, a system for data collection in an industrialenvironment may present, in an AR display, data being collected from aplurality of sensors in the industrial environment as one of a pluralitygraphical effects (e.g., colors in a range of colors) that correlate thedata being collected from each sensor to a scale of values within anacceptable range compared to values outside of the acceptable range. Inembodiments, the plurality of graphical effects may overlay a view ofthe industrial environment and placement of the plurality of graphicaleffects may correspond to locations in the view of the environment atwhich a sensor is located that is producing the corresponding sensordata. In embodiments, a first set of graphical effects (e.g., hotcolors) represent components for which multiple sensors indicate valuesoutside acceptable ranges.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, in an AR display informationbeing collected by sensors in the industrial environment as a heat mapoverlaying a visualization of the environment so that regions of theenvironment with sensor data suggestive of a greater potential offailure are overlaid with a graphic effect that is different thanregions of the environment with sensor data suggestive of a lesserpotential of failure. In embodiments, the heat map is based on datacurrently being sensed. In embodiments, the heat map is based on datafrom prior failures. In embodiments, the heat map is based on changes indata from an earlier period, such as data that suggest an increasedlikelihood of machine failure. In embodiments, the heat map is based ona preventive maintenance plan and a record of preventive maintenance inthe industrial environment.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting information being collected bysensors in the industrial environment as a heat map overlaying a view ofthe environment, such as a live view as may be presented in an ARdisplay. Such a system may include presenting an overlay thatfacilitates a call to action, wherein the overlay is associated with aregion of the heat map. The overlay may comprise a visual effect of apart or subsystem of the environment on which the action is to beperformed. In embodiments, the action to be performed is maintenancerelated and may be part-specific.

In embodiments, a system for data collection in an industrialenvironment may facilitate updating, in an AR view of a portion of theenvironment, a heat map of aspects of the industrial environment basedon a change to operating instructions for at least one aspect of amachine in the industrial environment. The heat map may representcompliance with operational limits for portions of machines in theindustrial environment. In embodiments, the heat map may represent alikelihood of component failure as a result of the change to operationinstructions.

In embodiments, a system for data collection in an industrialenvironment may facilitate presenting, as a heat map in an AR view of aportion of the environment, a degree or measure of coverage of sensorsin the industrial environment for a data collection template thatidentifies select sensors in the industrial environment for a datacollection activity.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map overlaying a view, suchas a live view, of an industrial environment of failure-related data forvarious portions of the environment. The failure-related data maycomprise a difference between an actual failure rate of the variousportions and another failure rate. Another failure rate may be a rate offailure of comparable portions elsewhere in the environment, and/oraverage failure rate of comparable portions across a plurality ofenvironments, such as an industry average, manufacturer failure rateestimate, and the like.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from robotic arms and hands for production line robotichandling in an augmented reality view of a portion of the environment. Aheat map related to data collected from robotic arms and hands mayrepresent data from sensors disposed in—for example, the fingers of arobotic hand. Sensor may collect data, such as applied pressure whenpinching an object, resistance (e.g., responsive to a robotic touch) ofan object, multi-axis forces presented to the finger as it performs anoperation, such as holding a tool and the like, temperature of theobject, total movement of the finger from initial point of contact untila resistance threshold is met, and other hand position/use conditions.Heat maps of this data may be presented in an augmented reality view ofa robotic production environment so that a user may make a visualassessment of, for example, how the relative positioning of the roboticfingers impacts the object being handled.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from linear bearings for production line robotic handling inan augmented reality view of a portion of the environment. Linearbearings, as with most bearings, may not be visible while in use.However, assessing their operation may benefit from representing datafrom sensors that capture information about the bearings while in use inan augmented reality display. In embodiments, sensors may be placed todetect forces being placed on portions of the bearings by the rotatingmember or elements that the bearings support. These forces may bepresented as heat maps that correspond to relative forces, on avisualization of the bearings in an augmented reality view of a robothandling machine that uses linear bearings.

In embodiments, a system for data collection in an industrialenvironment may facilitate displaying a heat map related to datacollected from boring machinery for mining in an augmented reality viewof a portion of the environment. Boring machinery, and in particularmulti-tip circular boring heads may experience a range of rockformations at the same time. Sensors may be placed proximal to eachboring tip that may detect forces experienced by the tips. The data maybe collected by a system adapted to collect data in an industrialenvironment and provided to an augmented reality system that may displaythe data as heat maps or the like in a view of the boring machine.

Referring to FIG. 78, an augmented reality display of heat maps based ondata collected in an industrial environment by a system adapted tocollect data in the environment is depicted. An augmented reality viewof an industrial environment 11500 may include heat maps 11502 thatdepict data received from or derived from data received from sensors11504 in the industrial environment. Sensor data may be captured andprocessed by a system adapted for data collection and analysis in anindustrial environment. The data may be converted into a form that issuitable for use in an augmented reality system for displaying heatmaps. The heat maps 11502 may be aligned in the augmented reality viewwith a sensor from which the underlying data was sourced.

Clause 1. In embodiments, an augmented reality (AR) system in whichindustrial machine sensed data is presented in a view of the industrialmachine as heat maps of data collected from sensors in the view, whereinthe heat maps are positioned proximal to a sensor capturing the senseddata that is visible in the AR display. 2. The system of clause 1,wherein the heat maps are based on a comparison of real time datacollected from sensors with an acceptable range of values for the data.3. The system of clause 1, wherein the heat maps are based on trends ofsensed data. 4. The system of clause 1, wherein the heat maps representa measure of coverage of sensors in the industrial environment inresponse to a condition of interest that is calculated from datacollected by sensors in the industrial environment. 5. The system ofclause 1, wherein the heat maps of data collected from sensors in theview is based on data collected by a system adapted to collect data inthe industrial environment by routing data from a plurality of sensorsto a plurality of data collectors via at least one of an analogcrosspoint switch, a multiplexer, and a hierarchical multiplexer. 6. Thesystem of clause 1, wherein the heat maps present different collecteddata values as different colors. 7. The system of clause 1, wherein datacollected from a plurality of sensors is combined to produce a heat map.8. A system for data collection in an industrial environment,comprising: an augmented reality display that presents data beingcollected from a plurality of sensors in the industrial environment asone of a plurality of colors, wherein the colors correlate the databeing collected from each sensor to a color scale with cool colorsmapping to values of the data within an acceptable range and hot colorsmapping to values of the data outside of the acceptable range, whereinthe plurality of colors overlay a view of the industrial environment andplacement of the plurality of colors corresponds to locations in theview of the environment at which a sensor is located that is producingthe corresponding sensor data. 9. The system of clause 8, wherein hotcolors represent components for which multiple sensors indicate valuesoutside typical ranges. 10. The system of clause 8, wherein theplurality of colors is based on a comparison of real time data collectedfrom sensors with an acceptable range of values for the data. 11. Thesystem of clause 8, wherein the plurality of colors is based on trendsof sensed data. 12. The system of clause 8, wherein the plurality ofcolors represents a measure of coverage of sensors in the industrialenvironment in response to a condition of interest that is calculatedfrom data collected by sensors in the industrial environment. 13. Amethod comprising, presenting information being collected by sensors inan industrial environment as a heat map overlaying a view of theenvironment so that regions of the environment with sensor datasuggestive of a greater potential of failure are overlaid with a heatmap that is different than regions of the environment with sensor datasuggestive of a lesser potential of failure. 14. The method of clause13, wherein the heat map is based on data currently being sensed. 15.The method of clause 13, wherein the heat map is based on data fromprior failure data. 16. The method of clause 13, wherein the heat map isbased on changes in data from an earlier period that suggest anincreased likelihood of machine failure. 17. The method of clause 13,wherein the heat map is based on a preventive maintenance plan and arecord of preventive maintenance in the industrial environment. 18. Themethod of clause 13, wherein the heat map represents an actual failurerate versus a reference failure rate. 19. The method of clause 18,wherein the reference failure rate is an industry average failure rate.20. The method of clause 18, wherein the reference failure rate is amanufacturer's failure rate estimate.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an augmented reality and/orvirtual reality (AR/VR) display in which data values output by sensorsdisposed in a field of view in the AR/VR display are displayed withvisual attributes that indicate a degree of compliance of the data to anacceptable range or values for the sensed data. In embodiments, thevisual attributes may provide near real-time portrayal of trends of thesensed data and/or of derivatives thereof. In embodiments, the visualattributes may be the actual data being captured, or the derived data,such as a trend of the data and the like.

In embodiments, a system for data collection and visualization thereofin an industrial environment may include an AR/VR display in whichtrends of data values output by sensors disposed in a field of view inthe AR/VR are displayed with visual attributes that indicate a degree ofseverity of the trend. In embodiments, other data or analysis that couldbe displayed may include: data from sensors that exceed an acceptablerange, data from sensors that are part of a smart band selected by theuser, data from sensors that are monitored for triggering a smart bandcollection action, data from sensors that sense an aspect of theenvironment that meets preventive maintenance criteria, such as a PMaction is upcoming soon, a PM action was recently performed or isoverdue for PM. Other data for such AR/VR visualization may include datafrom sensors for which an acceptable range has recently been changed,expanded, narrowed and the like. Other data for such AR/VR visualizationthat may be particularly useful for an operator of an industrial machine(digging, drilling, and the like) may include analysis of data fromsensors, such as for example impact on an operating element (torque,force, strain, and the like).

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for pumps in a mining application. Mining application pumps mayprovide water and remove liquefied waste from a mining site. Pumpperformance may be monitored by sensors detecting pump motors,regulators, flow meters, and the like. Pump performance monitoring datamay be collected and presented as a set of visual attributes in anaugmented reality display. In an example, pump motor power consumption,efficiency, and the like may be displayed proximal to a pump viewedthrough an augmented reality display.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for energy storage in a power generation application. Powergeneration energy storage may be monitored with sensors that capturedata related to storage and use of stored energy. Information such asutilization of individual energy storage cells, energy storage rate(e.g., battery charging and the like), stored energy consumption rate(e.g., KWH being supplied by an energy storage system), storage cellstatus, and the like may be captured and converted into augmentedreality viewable attributes that may be presented in an augmentedreality view of an energy storage system.

In embodiments, a system for data collection and visualization thereofin an industrial environment that may include presentation of visualattributes that represent collected data in an AR/VR environment may doso for feed water systems in a power generation application. Sensors maybe disposed in an industrial environment, such as power generation forcollecting data about feed water systems. Data from those sensors may becaptured and processed by the system for data collection. Results ofthis processing may include trends of the data, such as feed watercooling rates, flow rates, pressure and the like. These trends may bepresented on an augmented reality view of a feed water system byapplying a map of sensors with physical elements visible in the view andthen retrieving data from the mapped sensors. The retrieved data (andderivatives thereof) may be presented in the augmented reality view ofthe feed water system.

Referring to FIG. 79, an augmented reality display 11600 comprising realtime data 11602 overlaying a view of an industrial environment isdepicted. Sensors 11604 in the environment may be recognized by theaugmented reality system, such as by first detecting an industrialmachine, system, or part thereof with which the sensors are associated.Data from the sensors 11604 may be retrieved from a data repository,processed into trends, and presented in the augmented reality view 11600proximal to the sensors from which the data originates.

Clause 1: In embodiments, a system for data collection and visualizationthereof in an industrial environment in which data values output bysensors disposed in a field of view in an electronic display aredisplayed in the electronic display with visual attributes that indicatea degree of compliance of the data to an acceptable range or values forthe sensed data. 2. The system of clause 1, wherein the view in theelectronic display is a view in an augmented reality display of theindustrial environment. 3. The system of clause 1, wherein the visualattributes are indicative of a trend of the sensed data over timerelative to the acceptable range. 4. The system of clause 1, wherein thedata values are disposed in the electronic display proximal to thesensors from which the data values are output. 5. The system of clause1, wherein the visual attributes further comprise an indication of asmart band set of sensors associated with the sensor from which the datavalues are output. 6. A system for data collection and visualizationthereof in an industrial environment in which data values output byselect sensors disposed in an augmented reality view of the industrialenvironment are displayed with visual attributes that indicate a degreeof compliance of the data to an acceptable range or values for thesensed data. 7. The system of clause 6, wherein the sensors are selectedbased on a data collection template that facilitates configuring sensordata routing resources in the system. 8. The system of clause 7, whereinthe select sensors are indicated in the template as part of a group ofsmart band sensors. 9. The system of clause 7, wherein the selectsensors are sensors that are monitored for triggering a smart band datacollection action. 10. The system of clause 6, wherein the selectsensors are sensors that sense an aspect of the environment associatedwith preventive maintenance criteria. 11. The system of clause 6,wherein the visual attributes further indicate if the acceptable rangehas been expanded or narrowed within the past 72 hours. 12. A system fordata collection and visualization thereof in an industrial environmentin which trends of data values output by select sensors disposed in afield of view of the industrial environment depicted in an augmentedreality display are displayed with visual attributes that indicate adegree of severity of the trend. 13. The system of clause 12, whereinsensors are selected when data from the sensors exceed an acceptablerange of values. 14. The system of clause 14, wherein sensors areselected based on the sensors being part of a smart band group ofsensors. 15. The system of clause 12, wherein the visual attributesfurther indicate a compliance of the trend with an acceptable range ofdata values. 16. The system of clause 12, wherein the system for datacollection is adapted to route data from the select sensors to acontroller of the augmented reality display based on a data collectiontemplate that facilitates configuring routing resources of the systemfor data collection. 17. The system of clause 12, wherein the sensorsare selected in response to the sensor data being configured in a smartband data collection template as an indication for triggering a smartband data collection action. 18. The system of clause 12, wherein thesensors are selected in response to preventive maintenance criteria. 19.The system of clause 18, wherein the preventive maintenance criteria areselected from the list consisting of a preventive maintenance action isscheduled, a preventive maintenance action has been completed in thelast 72 hours, a preventive maintenance action is overdue.

In an aspect, and as illustrated in FIG. 80, a data collection system12004 can be arranged to collect data in an industrial environment12000, e.g., from one or more targets 12002. In the illustratedembodiments, the data collection system 12004 includes a group or“swarm” 12006 of data collectors 12008, a network 12010, a computingsystem 12012, and a database or data pool 12014. Each of the datacollectors 12008 can include one or more input sensors and becommunicatively coupled to any and all of the other components of thedata collection system 12004, as is partially illustrated by theconnecting arrows between components.

The targets 12002 can be any form of machinery or component thereof inan industrial environment 12000. Examples of such industrialenvironments 12000 include but are not limited to factories, pipelines,construction sites, ocean oil rigs, ships, airplanes or other aircraft,mining environments, drilling environments, refineries, distributionenvironments, manufacturing environments, energy source extractionenvironments, offshore exploration sites, underwater exploration sites,assembly lines, warehouses, power generation environments, and hazardouswaste environments, each of which may include one or more targets 12002.Targets 12002 can take any form of item or location at which a sensorcan obtain data. Examples of such targets 12002 include but are notlimited to machines, pipelines, equipment, installations, tools,vehicles, turbines, speakers, lasers, automatons, computer equipment,industrial equipment, and switches.

The self-organization functionality of the data collection system 12004can be performed at or by any of the components of the data collectionsystem 12004. In embodiments, a data collector 12008 or the swarm 12006of data collectors 12008 can self-organize without assistance from othercomponents and based on, e.g., the data sensed by its associated sensorsand other knowledge. In embodiments, the network 12010 can self-organizewithout assistance from other components and based on, e.g., the datasensed by the data collectors 12008 or other knowledge. Similarly, thecomputing system 12012 and/or the data pool 12014 without assistancefrom other components and based on, e.g., the data sensed by the datacollectors 12008 or other knowledge. It should be appreciated that anycombination or hybrid-type self-organization system can also beimplemented.

For example, only the data collection system 12004 can perform or enablevarious methods or systems for data collection having self-organizationfunctionality in an industrial environment 12000. These methods andsystems can include analyzing a plurality of sensor inputs, e.g.,received from or sensed by sensors at the data collector(s) 12008. Themethods and systems can also include sampling the received data andself-organizing at least one of: (i) a storage operation of the data;(ii) a collection operation of sensors that provide the plurality ofsensor inputs, and (iii) a selection operation of the plurality ofsensor inputs.

In aspects, the storage operation can include storing the data in alocal database, e.g., of a data collector 12008, a computing system12012, and/or a data pool 12014. The data can also be summarized over agiven time period to reduce a size of the sensed data. The summarizeddata can be sent to one or more data acquisition boxes, to one or moredata centers, and/or to other components of the system or other,separate systems. Summarizing the data over a given time period toreduce the size of the data, in some aspects, can include determining aspeed at which data can be sent via a network (e.g., network 12010),wherein the size of the summarized data corresponds to the speed atwhich data can be sent continuously in real time via the network. Insuch aspects, or others, the summarized data can be continuously sent,e.g., to an external device via the network.

In various implementations, the methods and systems can includecommitting the summarized data to a local ledger, identifying one ormore other accessible signal acquisition instruments on an accessiblenetwork, and/or synchronizing the summarized data at the local ledgerwith at least one of the other accessible signal acquisition instruments(e.g., data collectors 12008). In embodiments, receiving a remote streamof sensor data from one or more other accessible signal acquisitioninstruments via a network can be included. An advertisement message to apotential client indicating availability of at least one of the locallystored data, the summarized data, and the remote stream of sensor datacan also or alternatively be sent.

The methods and systems can include identifying one or more otheraccessible signal acquisition instruments (e.g., data collectors 12008)on an accessible network (e.g., 12010), nominating at least one of theone or more other accessible signal acquisition instruments as a logicalcommunication hub, and providing the logical communication hub with alist of available data and their associated sources. The list ofavailable data and their associated sources can be provided to thelogical communication hub utilizing a hybrid peer-to-peer communicationsprotocol.

In some aspects, the storage operation can include storing the data in alocal database and automatically organizing at least one parameter ofthe data pool utilizing machine learning. The organizing can be based atleast in part on receiving information regarding at least one of anaccuracy of classification and an accuracy of prediction of an externalmachine learning system that uses data from the data pool (e.g., datapool 12014).

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having self-organization functionality, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs; and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein thestorage operation includes storing the data in a local database, andsummarizing the data over a given time period to reduce a size of thedata.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending the summarized data to one or more data acquisition boxes.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending the summarized data to one or more data centers.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein summarizing the data over agiven time period to reduce the size of the data includes determining aspeed at which data can be sent via a network, wherein the size of thesummarized data corresponds to the speed at which data can be sentcontinuously in real time via the network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includescontinuously sending the summarized data to an external device via thenetwork.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs and self-organizing at least oneof: (i) a storage operation of the data; (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein thestorage operation includes storing the data in a local database,summarizing the data over a given time period to reduce a size of thedata, committing the summarized data to a local ledger, identifying oneor more other accessible signal acquisition instruments on an accessiblenetwork, and synchronizing the summarized data at the local ledger withat least one of the other accessible signal acquisition instruments. Afurther embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includesreceiving a remote stream of sensor data from one or more otheraccessible signal acquisition instruments via a network.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the method further includessending an advertisement message to a potential client indicatingavailability of at least one of the locally stored data, the summarizeddata, and the remote stream of sensor data.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs;

sampling data received from the sensor inputs, self-organizing at leastone of: (i) a storage operation of the data (ii) a collection operationof sensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein thestorage operation includes storing the data in a local database, andsummarizing the data over a given time period to reduce a size of thedata, identifying one or more other accessible signal acquisitioninstruments on an accessible network, nominating at least one of the oneor more other accessible signal acquisition instruments as a logicalcommunication hub, and providing the logical communication hub with alist of available data and their associated sources.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the list of available data andtheir associated sources is provided to the logical communication hubutilizing a hybrid peer-to-peer communications protocol.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein thestorage operation includes storing the data in a local database,summarizing the data over a given time period to reduce a size of thedata, storing the data in a local database, and automatically organizingat least one parameter of the database utilizing machine learning,wherein the organizing is based at least in part on receivinginformation regarding at least one of an accuracy of classification andan accuracy of prediction of an external machine learning system thatuses data from the database.

In aspects, the collection operation of sensors that provide theplurality of sensor inputs can include receiving instructions directinga mobile data collector unit (e.g., data collector 12008) to operatesensors at a target (e.g., 12002), wherein at least one of the pluralityof sensors is arranged in the mobile data collector unit. Acommunication can be transmitted to one or more other mobile datacollector units (12008) regarding the instructions. The swarm 12006 orportion thereof can self-organize a distribution of the mobile datacollector unit and the one or more other mobile data collector units(e.g., data collectors 12008) at the target 12002.

In aspects, self-organizing the distribution of the mobile datacollector units at the target 12002 comprises utilizing a machinelearning algorithm to determine a respective target location for each ofthe mobile data collector units. The machine learning algorithm canutilize one or more of a plurality of features to determine therespective target locations. Examples of the features can include:battery life of the mobile data collector units (data collectors 12008),a type of the target 12002 being sensed, a type of signal being sensed,a size of the target 12002, a number of mobile data collector units(data collectors 12008) needed to cover the target 12002, a number ofdata points needed for the target 12002, a success in prioraccomplishment of signal capture, information received from aheadquarters or other components from which the instructions arereceived, and historical information regarding the sensors operated atthe target 12002.

In implementations, self-organizing the distribution of the mobile datacollector unit and the one or more other mobile data collector units atthe target location can include proposing a target location for themobile data collector unit(s), transmitting the target location to atleast one other mobile data collector units, receiving confirmation thatthere is no contention for the target location, directing one of themobile data collector units to the target location, and collectingsensor data at the target location from the directed mobile datacollector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan also include, in certain embodiments, proposing a target locationfor the mobile data collector unit, transmitting the target location toat least one of the one or more other mobile data collector units,receiving a proposal for a new target location, directing the mobiledata collector unit to the new target location, and collecting sensordata at the new target location from the mobile data collector unit.

In additional or alternative aspects, self-organizing the distributionof the mobile data collector unit and the one or more other mobile datacollector units at the target location can comprise proposing a targetlocation for the mobile data collector unit, determining that at leastone of the one or more other mobile data collector units is at or movingto the target location, determining a new target location based on theat least one of the one or more other mobile data collector units beingat or moving to the target location, directing the mobile data collectorunit to the new target location, and collecting sensor data at the newtarget location from the mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan further comprise determining a type of the sensors to operate at thetarget 12002, receiving confirmation that there is no contention for thetype of sensors, directing the mobile data collector unit to operate thetype of sensors at the target 12002, and collecting sensor data from thetype of sensors at the target 12002 from the mobile data collector unit.

In aspects, self-organizing the distribution of the mobile datacollector unit and the one or more other mobile data collector units atthe target location can include determining a type of the sensors tooperate at the target, transmitting the type of the sensors to at leastone of the one or more other mobile data collector units, receiving aproposal for a new type of the sensors, directing the mobile datacollector unit to operate the new type of sensors at the target, andcollecting sensor data from the new type of sensors at the target fromthe mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the target locationcan include determining a type of the sensors to operate at the target,determining that at least one of the one or more other mobile datacollector units is operating or can operate the type of the sensors atthe target, determining a new type of the sensors based on the at leastone of the one or more other mobile data collector units operating orbeing capable of operating the type of the sensors at the target,directing the mobile data collector unit to operate the new type ofsensors at the target, and collecting sensor data from the new type ofsensors at the target from the mobile data collector unit.

Self-organizing the distribution of the mobile data collector unit andthe one or more other mobile data collector units at the targetlocation, in some implementations, can comprise utilizing a swarmoptimization algorithm to allocate areas of sensor responsibilityamongst the mobile data collector unit and the one or more other mobiledata collector units. Examples of the swarm optimization algorithminclude but are not limited to Genetic Algorithms (GA), Ant ColonyOptimization (ACO), Particle Swarm Optimization (PSO), DifferentialEvolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization(GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP),Evolution Strategy (ES), Evolutionary Programming (EP), FireflyAlgorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO), orcombinations thereof.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having automated self-organization, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs;

sampling data received from the sensor inputs and self-organizing atleast one of (i) a storage operation of the data, (ii) a collectionoperation of sensors that provide the plurality of sensor inputs, and(iii) a selection operation of the plurality of sensor inputs, whereinthe collection operation of sensors that provide the plurality of sensorinputs includes receiving instructions directing a mobile data collectorunit to operate sensors at a target, wherein at least one of theplurality of sensors is arranged in the mobile data collector unit,transmitting a communication to one or more other mobile data collectorunits regarding the instructions, and self-organizing a distribution ofthe mobile data collector unit and the one or more other mobile datacollector units at the target.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target includes utilizing a machinelearning algorithm to determine a respective target location for each ofthe mobile data collector units.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the machine learning algorithmutilizes one or more of a plurality of features to determine therespective target locations, the plurality of features including:battery life of the mobile data collector units, a type of the targetbeing sensed, a type of signal being sensed, a size of the target, anumber of mobile data collector units needed to cover the target, anumber of data points needed for the target, a success in prioraccomplishment of signal capture, information received from aheadquarters from which the instructions are received, and historicalinformation regarding the sensors operated at the target.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, transmitting thetarget location to at least one of the one or more other mobile datacollector units, receiving confirmation that there is no contention forthe target location, directing the mobile data collector unit to thetarget location, and collecting sensor data at the target location fromthe mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, transmitting thetarget location to at least one of the one or more other mobile datacollector units, receiving a proposal for a new target location,directing the mobile data collector unit to the new target location andcollecting sensor data at the new target location from the mobile datacollector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes proposing atarget location for the mobile data collector unit, determining that atleast one of the one or more other mobile data collector units is at ormoving to the target location, determining a new target location basedon the at least one of the one or more other mobile data collector unitsbeing at or moving to the target location, directing the mobile datacollector unit to the new target location and collecting sensor data atthe new target location from the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes determininga type of the sensors to operate at the target, receiving confirmationthat there is no contention for the type of sensors, directing themobile data collector unit to operate the type of sensors at the target,and collecting sensor data from the type of sensors at the target fromthe mobile data collector unit.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein thecollection operation of sensors that provide the plurality of sensorinputs includes receiving instructions directing a mobile data collectorunit to operate sensors at a target, wherein at least one of theplurality of sensors is arranged in the mobile data collector unit,transmitting a communication to one or more other mobile data collectorunits regarding the instructions, self-organizing a distribution of themobile data collector unit and the one or more other mobile datacollector units at the target, wherein self-organizing the distributionof the mobile data collector unit and the one or more other mobile datacollector units at the target location includes determining a type ofthe sensors to operate at the target, transmitting the type of thesensors to at least one of the one or more other mobile data collectorunits, receiving a proposal for a new type of the sensors, directing themobile data collector unit to operate the new type of sensors at thetarget and collecting sensor data from the new type of sensors at thetarget from the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes determininga type of the sensors to operate at the target, determining that atleast one of the one or more other mobile data collector units isoperating or can operate the type of the sensors at the target,determining a new type of the sensors based on the at least one of theone or more other mobile data collector units operating or being capableof operating the type of the sensors at the target, directing the mobiledata collector unit to operate the new type of sensors at the target,and collecting sensor data from the new type of sensors at the targetfrom the mobile data collector unit.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein self-organizing thedistribution of the mobile data collector unit and the one or more othermobile data collector units at the target location includes utilizing aswarm optimization algorithm to allocate areas of sensor responsibilityamongst the mobile data collector unit and the one or more other mobiledata collector units.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the swarm optimizationalgorithm is one or more types of Genetic Algorithms (GA), Ant ColonyOptimization (ACO), Particle Swarm Optimization (PSO), DifferentialEvolution (DE), Artificial Bee Colony (ABC), Glowworm Swarm Optimization(GSO), and Cuckoo Search Algorithm (CSA), Genetic Programming (GP),Evolution Strategy (ES), Evolutionary Programming (EP), FireflyAlgorithm (FA), Bat Algorithm (BA) and Grey Wolf Optimizer (GWO).

In aspects, the selection operation can comprise receiving a signalrelating to at least one condition of the industrial environment 12000and, based on the signal, changing at least one of the sensor inputsanalyzed and a frequency of the sampling. The at least one condition ofthe industrial environment can be a signal-to-noise ratio of the sampleddata. The selection operation can include identifying a target signal tobe sensed. Additionally, the selection operation further can includeidentifying one or more non-target signals in a same frequency band asthe target signal to be sensed and, based on the identified one or morenon-target signals, changing at least one of the sensor inputs analyzedand a frequency of the sampling.

The selection operation can comprise identifying other data collectorssensing in a same signal band as the target signal to be sensed, and,based on the identified other data collectors, changing at least one ofthe sensor inputs analyzed and a frequency of the sampling. Inimplementations, the selection operation can further compriseidentifying a level of activity of a target associated with the targetsignal to be sensed and, based on the identified level of activity,changing at least one of the sensor inputs analyzed and a frequency ofthe sampling.

The selection operation can further comprise receiving data indicativeof environmental conditions near a target associated with the targetsignal, comparing the received environmental conditions of the targetwith past environmental conditions near the target or another targetsimilar to the target, and, based on the comparison, changing at leastone of the sensor inputs analyzed and a frequency of the sampling. Atleast a portion of the received sampling data can be transmitted toanother data collector according to a predetermined hierarchy of datacollection.

The selection operation further comprises, in some aspects, receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and, based on the analysis of thereceived feedback, changing at least one of the sensor inputs analyzed,the frequency of sampling, the data stored, and the data transmitted.

Additionally, or alternatively, the selection operation can comprisereceiving data indicative of environmental conditions near a targetassociated with the target signal, transmitting at least a portion ofthe received sampling data to another data collector according to apredetermined hierarchy of data collection, receiving feedback via anetwork connection relating to one or more yield metrics of thetransmitted data, analyzing the received feedback, and, based on theanalysis of the received feedback, changing at least one of the sensorinputs analyzed, the frequency of sampling, the data stored, and thedata transmitted.

In implementations, the selection operation can include receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating topower utilization, analyzing the received feedback, and based on theanalysis of the received feedback, changing at least one of the sensorinputs analyzed, the frequency of sampling, the data stored, and thedata transmitted.

The selection operation can also or alternatively comprise receivingdata indicative of environmental conditions near a target associatedwith the target signal, transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection, receiving feedback via a networkconnection relating to a quality or sufficiency of the transmitted data,analyzing the received feedback, and, based on the analysis of thereceived feedback, executing a dimensionality reduction algorithm on thesensed data. The dimensionality reduction algorithm can be one or moreof a Decision Tree, Random Forest, Principal Component Analysis, FactorAnalysis, Linear Discriminant Analysis, Identification based oncorrelation matrix, Missing Values Ratio, Low Variance Filter, RandomProjections, Nonnegative Matrix Factorization, Stacked Auto-encoders,Chi-square or Information Gain, Multidimensional Scaling, CorrespondenceAnalysis, Factor Analysis, Clustering, and Bayesian Models. Thedimensionality reduction algorithm can be performed at a data collector12008, a swarm 12006 of data collectors 12008, a network 12010, acomputing system 12012, a data pool 12014, or combination thereof. Inaspects, executing the dimensionality reduction algorithm can comprisesending the sensed data to a remote computing device.

In aspects, a system for self-organizing collection and storage of datacollection in a power generation environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Such sensors can be a component of the data collector, externalto the data collector (e.g., external sensors or components of differentdata collector(s)), or a combination thereof. The plurality of sensorinputs can be configured to sense at least one of an operational mode, afault mode, and a health status of at least one target system. Examplesof such target systems include but are not limited to a fuel handlingsystem, a power source, a turbine, a generator, a gear system, anelectrical transmission system, a transformer, a fuel cell, and anenergy storage device/system. The system can also include aself-organizing system that can be configured for self-organizing atleast one of: (i) a storage operation of the data; (ii) a datacollection operation of the sensors that provide the plurality of sensorinputs, and (iii) a selection operation of the plurality of sensorinput, as is described herein.

In aspects, the system can include a swarm 12006 of mobile datacollectors (e.g., data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g.,network 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a turbineas a target system. Vibration sensors, temperature sensors, acousticsensors, strain gauges, and accelerometers, and the like may be utilizedby the system to generate data regarding the operation of the turbine.As mentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

In aspects, a system for self-organizing collection and storage of datacollection in energy source extraction environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Examples of such energy source extraction environments includea coal mining environment, a metal mining environment, a mineral miningenvironment, and an oil drilling environment, although other extractionenvironments are contemplated by the present disclosure. The sensorsutilized can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a hauling system, alifting system, a drilling system, a mining system, a digging system, aboring system, a material handling system, a conveyor system, a pipelinesystem, a wastewater treatment system, and a fluid pumping system.

The system can also include a self-organizing system that can beconfigured for self-organizing at least one of: (i) a storage operationof the data; (ii) a data collection operation of the sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor input, as is described herein. In aspects,the system can include a swarm 12006 of mobile data collectors (e.g.,data collectors 12008). Further, in additional or alternative aspects,the self-organizing system can generate, iterate, optimize, etc. astorage specification for organizing storage of the data. The storagespecification, e.g., can specify which data will be stored for localstorage in the power generation environment, and which data will beoutput for streaming via a network connection (e.g., network 12010) fromthe power generation environment. Other data collection, generation,and/or storage operations can be performed or enabled by the system, asis described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a fluidpumping system as a target system. Vibration sensors, flow sensors,pressure sensors, temperature sensors, acoustic sensors, and the likemay be utilized by the system to generate data regarding the operationof the fluid pumping system. As mentioned herein, any and all of thestorage operation, the data collection operation, and the selectionoperation of the plurality of sensor inputs may be adapted, optimized,learned, or otherwise self-organized by the system.

In implementations, a system for self-organizing collection and storageof data collection in a manufacturing environment can include a datacollector for handling a plurality of sensor inputs from varioussensors. Such sensors can be a component of the data collector, externalto the data collector (e.g., external sensors or components of differentdata collector(s)), or a combination thereof. The plurality of sensorinputs can be configured to sense at least one of an operational mode, afault mode, and a health status of at least one target system. Examplesof such target systems include but are not limited to a power system, aconveyor system, a generator, an assembly line system, a wafer handlingsystem, a chemical vapor deposition system, an etching system, aprinting system, a robotic handling system, a component assembly system,an inspection system, a robotic assembly system, and a semi-conductorproduction system. The system can also include a self-organizing systemthat can be configured for self-organizing at least one of: (i) astorage operation of the data; (ii) a data collection operation of thesensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor input, as is describedherein.

In aspects, the system can include a swarm 12006 of mobile datacollectors (e.g., data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g.,network 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the environment of a waferhandling system as a target system. Vibration sensors, fluid flowsensors, pressure sensors, gas sensors, temperature sensors, and thelike may be utilized by the system to generate data regarding theoperation of the wafer handling system. As mentioned herein, any and allof the storage operation, the data collection operation, and theselection operation of the plurality of sensor inputs may be adapted,optimized, learned, or otherwise self-organized by the system.

Also disclosed are embodiments of an additional or alternative systemfor self-organizing collection and storage of data collection inrefining environment. Such system(s) can include a data collector forhandling a plurality of sensor inputs from various sensors. Examples ofsuch refining environments include a chemical refining environment, apharmaceutical refining environment, a biological refining environment,and a hydrocarbon refining environment, although other refiningenvironments are contemplated by the present disclosure. The sensorsutilized can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a power system, apumping system, a mixing system, a reaction system, a distillationsystem, a fluid handling system, a heating system, a cooling system, anevaporation system, a catalytic system, a moving system, and a containersystem.

The system can also include a self-organizing system that can beconfigured for self-organizing at least one of: (i) a storage operationof the data; (ii) a data collection operation of the sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor input, as is described herein. In aspects,the system can include a swarm 12006 of mobile data collectors (e.g.,data collectors 12008). Further, in additional or alternative aspects,the self-organizing system can generate, iterate, optimize, etc. astorage specification for organizing storage of the data. The storagespecification, e.g., can specify which data will be stored for localstorage in the power generation environment, and which data will beoutput for streaming via a network connection (e.g., network 12010) fromthe power generation environment. Other data collection, generation,and/or storage operations can be performed or enabled by the system, asis described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the refining environment of aheating system as a target system. Temperature sensors, fluid flowsensors, pressure sensors, and the like may be utilized by the system togenerate data regarding the operation of the heating system. Asmentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

In aspects, a system for self-organizing collection and storage of datacollection in a distribution environment can include a data collectorfor handling a plurality of sensor inputs from various sensors. Suchsensors can be a component of the data collector, external to the datacollector (e.g., external sensors or components of different datacollector(s)), or a combination thereof. The plurality of sensor inputscan be configured to sense at least one of an operational mode, a faultmode, and a health status of at least one target system. Examples ofsuch target systems include but are not limited to a power system, aconveyor system, a robotic transport system, a robotic handling system,a packing system, a cold storage system, a hot storage system, arefrigeration system, a vacuum system, a hauling system, a liftingsystem, an inspection system, and a suspension system. The system canalso include a self-organizing system that can be configured forself-organizing at least one of: (i) a storage operation of the data;(ii) a data collection operation of the sensors that provide theplurality of sensor inputs, and (iii) a selection operation of theplurality of sensor input, as is described herein.

In aspects, the system can include a swarm 12006 of mobile datacollectors (e.g., data collectors 12008). Further, in additional oralternative aspects, the self-organizing system can generate, iterate,optimize, etc. a storage specification for organizing storage of thedata. The storage specification, e.g., can specify which data will bestored for local storage in the power generation environment, and whichdata will be output for streaming via a network connection (e.g.,network 12010) from the power generation environment. Other datacollection, generation, and/or storage operations can be performed orenabled by the system, as is described herein.

In a non-limiting example, the system can include a plurality of sensorsconfigured to sense various parameters in the distribution environmentof a refrigeration system as a target system. Power sensors, temperaturesensors, vibration sensors, strain gauges, and the like may be utilizedby the system to generate data regarding the operation of the turbine.As mentioned herein, any and all of the storage operation, the datacollection operation, and the selection operation of the plurality ofsensor inputs may be adapted, optimized, learned, or otherwiseself-organized by the system.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

The present disclosure describes a system for data collection in anindustrial environment having automated self-organization, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the industrial environment and forgenerating data associated with the plurality of sensor inputs, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of sensors thatprovide the plurality of sensor inputs, and (iii) a selection operationof the plurality of sensor inputs.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes receiving a signal relating to at least onecondition of the industrial environment, based on the signal, changingat least one of the sensor inputs analyzed and a frequency of thesampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the at least one condition ofthe industrial environment is a signal-to-noise ratio of the sampleddata.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationincludes identifying a target signal to be sensed.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying one or more non-target signals in a samefrequency band as the target signal to be sensed, and based on theidentified one or more non-target signals, changing at least one of thesensor inputs analyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying other data collectors sensing in a samesignal band as the target signal to be sensed, and based on theidentified other data collectors, changing at least one of the sensorinputs analyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes identifying a level of activity of a target associatedwith the target signal to be sensed, and based on the identified levelof activity, changing at least one of the sensor inputs analyzed and afrequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes receiving data indicative of environmental conditionsnear a target associated with the target signal, comparing the receivedenvironmental conditions of the target with past environmentalconditions near the target or another target similar to the target, andbased on the comparison, changing at least one of the sensor inputsanalyzed and a frequency of the sampling.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the selection operationfurther includes transmitting at least a portion of the receivedsampling data to another data collector according to a predeterminedhierarchy of data collection.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes identifying a target signal to be sensed,receiving a signal relating to at least one condition of the industrialenvironment, based on the signal, changing at least one of the sensorinputs analyzed and a frequency of the sampling, receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating toa quality or sufficiency of the transmitted data, analyzing the receivedfeedback, and based on the analysis of the received feedback, changingat least one of the sensor inputs analyzed, the frequency of sampling,the data stored, and the data transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes identifying a target signal to be sensed,receiving a signal relating to at least one condition of the industrialenvironment, based on the signal, changing at least one of the sensorinputs analyzed and a frequency of the sampling, receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating toone or more yield metrics of the transmitted data, analyzing thereceived feedback, and based on the analysis of the received feedback,changing at least one of the sensor inputs analyzed, the frequency ofsampling, the data stored, and the data transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes identifying a target signal to be sensed,receiving a signal relating to at least one condition of the industrialenvironment, based on the signal, changing at least one of the sensorinputs analyzed and a frequency of the sampling, receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback, via a network connection relatingto power utilization, analyzing the received feedback, and based on theanalysis of the received feedback, changing at least one of the sensorinputs analyzed, the frequency of sampling, the data stored, and thedata transmitted.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes identifying a target signal to be sensed,receiving a signal relating to at least one condition of the industrialenvironment, based on the signal, changing at least one of the sensorinputs analyzed and a frequency of the sampling, receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating toa quality or sufficiency of the transmitted data, analyzing the receivedfeedback, and based on the analysis of the received feedback, executinga dimensionality reduction algorithm on the sensed data.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the dimensionality reductionalgorithm is one or more of a Decision Tree, Random Forest, PrincipalComponent Analysis, Factor Analysis, Linear Discriminant Analysis,Identification based on correlation matrix, Missing Values Ratio, LowVariance Filter, Random Projections, Nonnegative Matrix Factorization,Stacked Auto-encoders, Chi-square or Information Gain, MultidimensionalScaling, Correspondence Analysis, Factor Analysis, Clustering, andBayesian Models.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the dimensionality reductionalgorithm is performed at a data collector.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein executing the dimensionalityreduction algorithm includes sending the sensed data to a remotecomputing device.

The present disclosure describes a method for data collection in anindustrial environment having self-organization functionality, themethod according to one disclosed non-limiting embodiment of the presentdisclosure can include analyzing a plurality of sensor inputs, samplingdata received from the sensor inputs, and self-organizing at least oneof (i) a storage operation of the data, (ii) a collection operation ofsensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs, wherein theselection operation includes identifying a target signal to be sensed,receiving a signal relating to at least one condition of the industrialenvironment, based on the signal, changing at least one of the sensorinputs analyzed and a frequency of the sampling, receiving dataindicative of environmental conditions near a target associated with thetarget signal, transmitting at least a portion of the received samplingdata to another data collector according to a predetermined hierarchy ofdata collection, receiving feedback via a network connection relating toat least one of a bandwidth and a quality or of the network connection,analyzing the received feedback, and based on the analysis of thereceived feedback, changing at least one of the sensor inputs analyzed,the frequency of sampling, the data stored, and the data transmitted.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a power generation environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment, whereinthe plurality of sensor inputs is configured to sense at least one of anoperational mode, a fault mode, and a health status of at least onetarget system selected from a group consisting of a fuel handlingsystem, a power source, a turbine, a generator, a gear system, anelectrical transmission system, and a transformer, and a self-organizingsystem for self-organizing at least one of (i) a storage operation ofthe data, (ii) a data collection operation of the sensors that providethe plurality of sensor inputs, and (iii) a selection operation of theplurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing storage of the data,the storage specification specifying data for local storage in the powergeneration environment and specifying data for streaming via a networkconnection from the power generation environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in an energy source extractionenvironment, the system according to one disclosed non-limitingembodiment of the present disclosure can include a data collector forhandling a plurality of sensor inputs from sensors in the energyextraction environment, wherein the plurality of sensor inputs isconfigured to sense at least one of an operational mode, a fault mode,and a health status of at least one target system selected from a groupconsisting of a hauling system, a lifting system, a drilling system, amining system, a digging system, a boring system, a material handlingsystem, a conveyor system, a pipeline system, a wastewater treatmentsystem, and a fluid pumping system, and a self-organizing system forself-organizing at least one of (i) a storage operation of the data,(ii) a data collection operation of the sensors that provide theplurality of sensor inputs, and (iii) a selection operation of theplurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing storage of the data,the storage specification specifying data for local storage in theenergy extraction environment and specifying data for streaming via anetwork connection from the energy extraction environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a coal mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a metal mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is a mineral mining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the energy source extractionenvironment is an oil drilling environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a manufacturing environment, thesystem according to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment, whereinthe plurality of sensor inputs is configured to sense at least one of anoperational mode, a fault mode, and a health status of at least onetarget system selected from a group consisting of a power system, aconveyor system, a generator, an assembly line system, a wafer handlingsystem, a chemical vapor deposition system, an etching system, aprinting system, a robotic handling system, a component assembly system,an inspection system, a robotic assembly system, and a semi-conductorproduction system, and a self-organizing system for self-organizing atleast one of (i) a storage operation of the data, (ii) a data collectionoperation of the sensors that provide the plurality of sensor inputs,and (iii) a selection operation of the plurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in themanufacturing environment and specifying data for streaming via anetwork connection from the manufacturing environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a refining environment, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the power generation environment, whereinthe plurality of sensor inputs is configured to sense at least one of anoperational mode, a fault mode and a health status of at least onetarget system selected from a group consisting of a power system, apumping system, a mixing system, a reaction system, a distillationsystem, a fluid handling system, a heating system, a cooling system, anevaporation system, a catalytic system, a moving system, and a containersystem, and a self-organizing system for self-organizing at least one of(i) a storage operation of the data, (ii) a data collection operation ofthe sensors that provide the plurality of sensor inputs, and (iii) aselection operation of the plurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in therefining environment and specifying data for streaming via a networkconnection from the refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is achemical refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is apharmaceutical refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is abiological refining environment.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the refining environment is ahydrocarbon refining environment.

The present disclosure describes a system for self-organizing collectionand storage of data collection in a distribution environment, the systemaccording to one disclosed non-limiting embodiment of the presentdisclosure can include a data collector for handling a plurality ofsensor inputs from sensors in the distribution environment, wherein theplurality of sensor inputs is configured to sense at least one of anoperational mode, a fault mode and a health status of at least onetarget system selected from a group consisting of a power system, aconveyor system, a robotic transport system, a robotic handling system,a packing system, a cold storage system, a hot storage system, arefrigeration system, a vacuum system, a hauling system, a liftingsystem, an inspection system, and a suspension system, and aself-organizing system for self-organizing at least one of (i) a storageoperation of the data, (ii) a data collection operation of the sensorsthat provide the plurality of sensor inputs, and (iii) a selectionoperation of the plurality of sensor inputs.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemorganizes a swarm of mobile data collectors to collect data from aplurality of target systems.

A further embodiment of any of the foregoing embodiments of the presentdisclosure may include situations wherein the self-organizing systemgenerates a storage specification for organizing the storage of thedata, the storage specification specifying data for local storage in thedistribution environment and specifying data for streaming via a networkconnection from the distribution environment.

Referencing FIG. 83, various aspects of an example data storage profile12532 are depicted. The example data storage profile 12532 includesaspects of the data storage profile 12532 that may be included asadditional or alternative aspects of the data storage profile 12532relative to the storage location definition 12534, the storage timedefinition, and/or the storage time definition 12536, data resolutiondescription 12540, and/or may be included as aspects of these. Any oneor more of the factors or parameters relating to storage depicted inFIG. 179 may be included in a data storage profile 12532 and/or managedby a self-organizing storage system (e.g., system 12500 and/orcontroller 12532). The self-organizing storage system may manage oroptimize any such parameters or factors noted throughout thisdisclosure, individually or in combination, using an expert system,which may involve a rule-based optimization, optimization based on amodel of performance, and/or optimization using machinelearning/artificial intelligence, optionally including deep learningapproaches, or a hybrid or combination of the above. In embodiments, anexample data storage profile 12532 includes a storage type plan 12576 orprofile that accounts for or specifies a type of storage, such as basedon the underlying physical media type of the storage, the type of deviceor system on which storage resides, the mechanism by which storage canbe accessed for reading or writing data, or the like. For example, astorage media plan 12578 may specify or account for use of tape media,hard disk drive media, flash memory media, non-volatile memory, opticalmedia, one-time programmable memory, or the like. The storage media planmay account for or specify parameters relating to the media, includingcapabilities such as storage duration, power usage, reliability,redundancy, thermal performance factors, robustness to environmentalconditions (such as radiation or extreme temperatures), input/outputspeeds and capabilities, writing speeds, reading speeds, and the like,or other media specific parameters such as data file organization,operating system, read-write life cycle, data error rates, and/or datacompression aspects related to or inherent to the media or mediacontroller. A storage access plan 12580 or profile may specify oraccount for the nature of the interface to available storage, such asdatabase storage (including relational, object-oriented, and otherdatabases, as well as distributed databases, virtual machines,cloud-based databases, and the like), cloud storage (such as S3™ bucketsand other simple storage formats), stream-based storage, cache storage,edge storage (e.g., in edge-based network nodes), on-device storage,server-based storage, network-attached storage or the like. The storageaccess plan or profile may specify or account for factors such as thecost of different storage types, input/output performance, reliability,complexity, size, and other factors. A storage protocol plan 12582 orprofile may specify or account for a protocol by which data will betransmitted or written, such as a streaming protocol, an IP-basedprotocol, a non-volatile memory express protocol, a SATA protocol orother network-attached storage protocol, a disk-attached storageprotocol, an Ethernet protocol, a peered storage protocol, a distributedledger protocol, a packet-based storage protocol, a batch-based storageprotocol, a metadata storage protocol, a compressed storage protocol(using various compression types, such as for packet-based media,streaming media, lossy or lossless compression types, and the like), orothers. The storage protocol plan may account for or specify factorsrelating to the storage protocol, such as input/output performance,compatibility with available network resources, cost, complexity, dataprocessing required to implement the protocol, network utilization tosupport the protocol, robustness of the protocol to support system noise(e.g., EM, competing network traffic, interruption frequency of networkavailability), memory utilization to implement the protocol (such as:as-stored memory utilization, and/or intermediate memory utilization increating or transferring the data), and the like. A storage writingprotocol 12584 plan or profile may specify or account for how data willbe written to storage, such as in file form, in streaming form, in batchform, in discrete chunks, to partitions, in stripes or bands acrossdifferent storage locations, in streams, in packets or the like. Thestorage writing protocol may account for or specify parameters andfactors relating to writing, such as input speed, reliability,redundancy, security, and the like. A storage security plan 12586 orprofile may account for or specify how storage will be secured, such asavailability or type of password protection, authentication,permissioning, rights management, encryption (of the data, of thestorage media, and/or of network traffic on the system), physicalisolation, network isolation, geographic placement, and the like. Astorage location plan 12588 or profile may account for or specify alocation for storage, such as a geolocation, a network location (e.g.,at the edge, on a given server, or within a given cloud platform orplatforms), or a location on a device, such as a location on a datacollector, a location on a handheld device (such as a smart phone,tablet, or personal computer of an operator within an environment), alocation within or across a group of devices (such as a mesh, apeer-to-peer group, a ring, a hub-and-spoke group, a set of paralleldevices, a swarm of devices (such as a swarm of collectors), or thelike), a location in an industrial environment (such as or within anstorage element of an instrumentation system of or for a machine, alocation on an information technology system for the environment, or thelike), or a dedicated storage system, such as a disk, dongle, USBdevice, or the like. A storage backup plan 12590 or profile may accountfor or specify a plan for backup or redundancy of stored data, such asindicating redundant locations and managing any or all of the abovefactors for a backup storage location. In certain embodiments, thestorage security plan 12586 and/or storage backup plan 12590 may specifyparameters such as data retention, long-term storage plans (e.g.,migrate the stored data to a different storage media after a period oftime and/or after certain operations in the system are performed on thedata), physical risk management of the data and/or storage media (e.g.,provision of the data in multiple geographic regions having distinctphysical risk parameters, movement of the data when a storage locationexperiences a physical risk, refreshing the data according to apredicted life cycle of a long-term storage media, etc.).

The example controller 12512 further includes a sensor data storageimplementation circuit 12526 that stores at least a portion of thenumber of sensor data values in response to the data storage profile12532. An example controller 12512 includes the data storage profile12532 having a storage location definition 12534 corresponding to atleast one of the number of sensor data values 12542, including at leastone location such as: a sensor storage location (e.g., data stored for aperiod of time on the sensor, and/or on a portable device for a user12518 in proximity to the industrial system 12502 where the portabledevice is adapted by the system as a sensor), a sensor communicationdevice storage location (e.g., a data controller 12508, MUX device,smart sensor in communication with other sensors, and/or on a portabledevice for a user 12518 in proximity to the industrial system 12502 or anetwork of the industrial system 12502 where the portable device isadapted by the system as a communication device to transfer sensor databetween components in the system, etc.), a regional network storagelocation (e.g., on a plant computer 12510 and/or controller 12512),and/or a global network storage location (e.g., on a cloud computingdevice 12514).

An example controller 12512 includes the data storage profile 12532including a storage time definition 12536 corresponding to at least oneof the number of sensor data values 12542, including at least one timevalue such as: a time domain description over which the corresponding atleast one of the number of sensor data values is to be stored (e.g.,times and locations for the data, which may include relative time tosome aspect such as the time of data sampling, a process stage start orstop time, etc., or an absolute time such as midnight, Saturday, thefirst of the month, etc.); a time domain storage trajectory including anumber of time values corresponding to a number of storage locationsover which the corresponding at least one of the number of sensor datavalues is to be stored (e.g., the flow of the sensor data through thesystem across a number of devices, with the time for each storagetransfer including a relative or absolute time description); a processdescription value over which the corresponding at least one of thenumber of sensor data values is to be stored (e.g., including a processdescription and the planned storage location for data values during thedescribed process portion; the process description can include stages ofa process, and identification of which process is related to the storageplan, and the like); and/or a process description trajectory including anumber of process stages corresponding to a number of storage locationsover which the corresponding at least one of the number of sensor datavalues is to be stored (e.g., the flow of the sensor data through thesystem across a number of devices, with process stage and/or processidentification for each storage transfer).

An example controller 12512 includes the data storage profile 12532including a data resolution description 12540 corresponding to at leastone of the number of sensor data values 12544, where the data resolutiondescription 12540 includes a value such as: a detection density valuecorresponding to the at least one of the number of sensor data values(e.g., detection density may be time sampling resolution, spatialsampling resolution, precision of the sampled data, and/or a processingoperation to be applied that may affect the available resolution, suchas filtering and/or lossy compression of the data); a detection densityvalue corresponding to a more than one of the number of the sensor datavalues (e.g., a group of sensors having similar detection densityvalues, a secondary data value determined from a group of sensors havinga specified detection density value, etc.); a detection densitytrajectory including a number of detection density values of the atleast one of the number of sensor data values, each of the number ofdetection density values corresponding to a time value (e.g., any of thedetection density concepts combined with any of the time domainconcepts); a detection density trajectory including a number ofdetection density values of the at least one of the number of sensordata values, each of the number of detection density valuescorresponding to a process stage value (e.g., any of the detectiondensity concepts combined with any of the process description or stageconcepts); and/or a detection density trajectory comprising a number ofdetection density values of the at least one of the number of sensordata values, each of the number of detection density valuescorresponding to a storage location value (e.g., detection density canbe varied according to the device storing the data).

An example sensor data storage profile circuit 12524 further updates thedata storage profile 12532 after the operations of the sensor datastorage implementation circuit 12526, where the sensor data storageimplementation circuit 12526 further stores the portion of the number ofsensor data values 12544 in response to the updated data storage profile12532. For example, during operations of a system at a first point intime, the sensor data storage implementation circuit 12526 utilizes acurrently existing data storage profile sensor data storageimplementation circuit 12526, which may be based on initial estimates ofthe system performance, desired data from an operator of the system,and/or from a previous operation of the sensor data storage profilecircuit 12524. During operations of the system, the sensor data storageimplementation circuit 12526 stores data according to the data storageprofile 12532, and the sensor data storage profile circuit 12524determines parameters for the data storage profile 12532 which mayresult in improved performance of the system. An example sensor datastorage profile circuit 12524 tests various parameters for the datastorage profile 12532, for example utilizing a machine learningoptimization routine, and upon determining that an improved data storageprofile 12532 is available, the sensor data storage profile circuit12524 provides the updated data storage profile 12532 which is utilizedby the sensor data storage implementation circuit 12526. In certainembodiments, the sensor data storage profile circuit 12524 may performvarious operations such as supplying an intermediate data storageprofile 12532 which is utilized by the sensor data storageimplementation circuit 12526 to produce real-world results, appliesmodeling to the system (either first principles modeling based on systemcharacteristics, a model utilizing actual operating data for the system,a model utilizing actual operating data for an offset system, and/orcombinations of these) to determine what an outcome of a given datastorage profile 12532 will be or would have been (including, forexample, taking extra sensor data beyond what is utilized to support aprocess operated by the system), and/or applying randomized changes tothe data storage profile 12532 to ensure that an optimization routinedoes not settle into a local optimum or non-optimal condition.

An example sensor data storage profile circuit 12524 further updates thedata storage profile 12532 in response to external data 12544 and/orcloud-based data 12538, including data such as: an enhanced data requestvalue (e.g., an operator, model, optimization routine, and/or otherprocess requests enhanced data resolution for one or more parameters); aprocess success value (e.g., indicating that current storage practiceprovides for sufficient data availability and/or system performance;and/or that current storage practice may be over-capable, and one ormore changes to reduce system utilization may be available); a processfailure value (e.g., indicating that current storage practices may notprovide for sufficient data availability and/or system performance,which may include additional operations or alerts to an operator todetermine whether the data transmission and/or availability contributedto the process failure); a component service value (e.g., an operationto adjust the data storage to ensure higher resolution data is availableto improve a learning algorithm predicting future service events, and/orto determine which factors may have contributed to premature service); acomponent maintenance value (e.g., an operation to adjust the datastorage to ensure higher resolution data is available to improve alearning algorithm predicting future maintenance events, and/or todetermine which factors may have contributed to premature maintenance);a network description value (e.g., a change in the network, for exampleby identification of devices, determination of protocols, and/or asentered by a user or operator, where the network change results in acapability change and potentially a distinct optimal storage plan forsensor data); a process feedback value (e.g., one or more processconditions detected); a network feedback value (e.g., one or morenetwork changes as determined by actual operations of the network—e.g.,a loss or reduction in communication of one or more devices, a networkcommunication volume change, a transmission noise value change on thenetwork, etc.); a sensor feedback value (e.g., metadata such as a sensorfault, capability change; and/or based on the detected data from thesystem, for example an anomalous reading, rate of change, or off-nominalcondition indicating that enhanced or reduced resolution, sampling time,etc. should change the storage plan); and/or a second data storageprofile, where the second data storage profile was generated for anoffset system.

An example storage planning circuit 12528 determines a dataconfiguration plan 12546 and updates the data storage profile 12532 inresponse to the data configuration plan 12546, where the sensor datastorage implementation circuit 12526 further stores at least a portionof the number of sensor data values in response to the updated datastorage profile 12532. An example data configuration plan 12546 includesa value such as: a data storage structure value (e.g., a data type, suchas integer, string, a comma delimited file, how many bits are committedto the values, etc.); a data compression value (e.g., whether tocompress data, a compression model to use, and/or whether segments ofdata can be replaced with summary information, polynomial or other curvefit summarizations, etc.); a data write strategy value (e.g., whether tostore values in a distributed manner or on a single device, whichnetwork communication and/or operating system protocols to utilize); adata hierarchy value (e.g., which data is favored over other data wherestorage constraints and/or communication constraints will limit thestored data—the limits may be temporal, such as data will not be in theintended location at the intended time, or permanent, such as some datawill need to be compressed in a lossy manner, and/or lost); an enhancedaccess value determined for the data (e.g., the data is of a type forreports, searching, modeling access, and/or otherwise tagged, whereenhanced access includes where the data is stored for scope ofavailability, indexing of data, summarization of data, topical reportsof data, which may be stored in addition to the raw or processed sensordata); and/or an instruction value corresponding to the data (e.g., aplaceholder indicating where data can be located, an interface to accessthe data, metadata indicating units, precision, time frames, processesin operation, faults present, outcomes, etc.).

It can be seen that the provision of control over data flow and storagethrough the system allows for improvement generally, and movement towardoptimization over time, of data management throughout the system.Accordingly, more data of a higher resolution can be accumulated, and ina more readily accessible manner, than previously known systems withfixed or manually configurable data storage and flow for a givenutilization of resources such as storage space, communication bandwidth,power consumption, and/or processor execution cycles. Additionally, thesystem can respond to process variations that affect the optimal orbeneficial parameters for controlling data flow and storage. One ofskill in the art, having the benefit of the disclosures herein, willrecognize that combinations of control of data storage schemes with datatype control and knowledge about process operations for a system createpowerful combinations in certain contemplated embodiments. For example,data of a higher resolution can be maintained for a longer period andmade available if a need for the data arises, without incurring the fullcost of storing the data permanently and/or communicating the datathroughout every layer of the system.

Referencing FIG. 81, an example storage time definition 12536 isdepicted. The example storage time definition 12536 depicts a number ofstorage locations 12556 corresponding to a number of time values 12558.It is understood that any values such as storage types, storage media,storage access, storage protocols, storage writing values, storagesecurity, and/or storage backup values, may be included in the storagetime definition 12536. Additionally or alternatively, an example storagetime definition 12536 may include process operations, events, and/orother values in addition to or as an alternative to time values 12558.The example storage time definition 12536 depicts movement of relatedsensor data to a first storage location 12550 over a first timeinterval, to a second storage location 12552 over a second timeinternal, and to a third storage location 12554 over a third timeinterval. The storage location values 12550, 12552, 12554 are depictedas an integral selection corresponding to planned storage locations, butadditionally or alternatively the values may be continuous or discrete,but not necessarily integral values. For example, a storage locationvalue 12550 of “1” may be associated with a first storage location, anda storage location value 12550 of “2” may be associated with a secondstorage location, where a value between “1” and “2” has an understoodmeaning—such as a prioritization to move the data (e.g., a “1.1”indicates that the data should be moved from “2” to “1” with arelatively high priority compared to a “1.4”), a percentage of the datato be moved (e.g., to control network utilization, memory utilization,or the like during a transfer operation), and/or a preference for astorage location with alternative options (e.g., to allow for directingstorage location, and inclusion in a cost function such that storagelocation can be balanced with other constraints in the system).Additionally or alternatively, the storage time definition 12536 caninclude additional dimensions (e.g., changing protocols, media, securityplans, etc.) and/or can include multiple options for the storage plan(e.g., providing a weighted value between 2, 3, 4, or more storagelocations, protocols, media, etc. in a triangulated ormultiple-dimension definition space).

Referencing FIG. 82 an example data resolution description 12540 isdepicted. The example data resolution description 12540 depicts a numberof data resolution values 12562 corresponding to a number of time values12564. It is understood that any values such as storage types, storagemedia, storage access, storage protocols, storage writing values,storage security, and/or storage backup values, may be included in thedata resolution description 12540. Additionally or alternatively, anexample data resolution description 12540 may include processoperations, events, and/or other values in addition to or as analternative to time values 12558. The example data resolutiondescription 12540 depicts changes in the resolution of stored relatedsensor data resolution values 12560 over time intervals, for exampleoperating at a low resolution initially, stepping up to a higherresolution (e.g., corresponding to a process start time), to a highresolution value (e.g., during a process time where the process issignificantly improved by high resolution of the related sensor data),and to a low resolution value (e.g., after a completion of the process).The example depicts a higher resolution before the process starts thanafter the process ends as an illustrative example, but the dataresolution description 12540 may include any data resolution trajectory.The data resolution values 12560 are depicted as integral selectionscorresponding to planned data resolutions, but additionally oralternatively the values may be continuous or discrete, but notnecessarily integral values. For example, data resolution values 12560of “1” may be associated with a first data resolution (e.g., a specificsampling time, byte resolution, etc.), and a data resolution values12560 of “2” may be associated with a second data resolution, where avalue between “1” and “2” has an understood meaning—such as aprioritization to sample at the defined resolution (e.g., a “1.1”indicates the data should be taken at a sampling rate corresponding to“1” with a relatively high priority compared to a “1.3”, and/or at asampling rate 10% of the way between the rate between “1” and “2”),and/or a preference for a data resolution with alternative options(e.g., to allow for sensor or network limitations, available sensorcommunication devices such as a data controller, smart sensor, orportable device taking the data from the sensor, and/or inclusion in acost function such that data resolution can be balanced with otherconstraints in the system). Additionally or alternatively, the dataresolution description 12540 can include additional dimensions (e.g.,changing protocols, media, security plans, etc.) and/or can includemultiple options for the data resolution plan (e.g., providing aweighted value between 2, 3, 4, or more data resolution values,protocols, media, etc. in a triangulated or multiple-dimensiondefinition space).

An example system 12500 further includes a haptic feedback circuit 12530that determines a haptic feedback instruction 12548 in response to atleast one of the number of sensor values 12542 and/or the data storageprofile 12532, and a haptic feedback device 12516 responsive to thehaptic feedback instruction 12548. Example and non-limiting hapticfeedback instructions 12548 include an instruction such as: a vibrationcommand; a temperature command; a sound command; an electrical command;and/or a light command. Example and non-limiting operations of thehaptic feedback circuit 12530 include feedback that data is stored orbeing stored on the haptic feedback device 12516 and/or on a portabledevice associated with the user 12518 in communication with the hapticfeedback device 12516 (e.g., user 12518 traverses through the system12500 with a smart phone, which the system 12500 utilizes to storesensor data, and provides a haptic feedback instructions 12548 to notifythe user 12518 that the smart phone is currently being utilized by thesystem 12500, for example allowing the user 12518 to remain incommunication with the sensor, data controller, or other transmittingdevice, and/or allowing the user to actively cancel or enable the datatransfer). Additionally or alternatively, the haptic feedback device12516 may be the smart phone (e.g., utilizing vibration, sound, light,or other haptic aspects of the smart phone), and/or the haptic feedbackdevice 12516 may include data storage and/or communication capabilities.

In certain embodiments, the haptic feedback circuit 12530 provides ahaptic feedback instruction 12548 as an alert or notification to theuser 12518, for example to alert or notify the user 12518 that a processhas commenced or is about to start, that an off-nominal operation isdetected or predicted, that a component of the system requires or ispredicted to require maintenance, that an aspect of the system is in acondition that the user 12518 may want to be aware of (e.g., a componentis still powered, has high potential energy of any type, is at a highpressure, and/or is at a high temperature where the user 12518 may be inproximity to the component), that a data storage related aspect of thesystem is in a noteworthy condition (e.g., a data storage component ofthe system is at capacity, out of communication, is in a faultcondition, has lost contact with a sensor, etc.), to request a responsefrom the user 12518 (e.g., an approval to start a process, datatransfer, process rate change, clear a fault, etc.) In certainembodiments, the haptic feedback circuit 12530 configures the hapticfeedback instruction 12548 to provide an intuitive feedback to the user12518. For example, an alert value may provide a more rapid, urgent,and/or intermittent vibration mode relative to an informationalnotification; a temperature based alert or notification may utilize atemperature based haptic feedback (e.g., an overtemperature vesselnotification may provide a warm or cold haptic feedback) and/or flashinga color that is associated with the temperature (e.g., flashing red foran overtemperature or blue for an under-temperature); an electricallybased notification may provide an electrically associated hapticfeedback (e.g., a sound associated with electricity such as a buzzing orsparking sound, or even a mild electrical feedback such as when a useris opening a panel for a component that is still powered); providing avibration feedback for a bearing, motor, or other rotating or vibratingcomponent that is operating off-nominally; and/or providing a requestedfeedback to the user based upon sensed data (e.g., transmitting avibration profile to the haptic feedback device that is analogous to thedetected vibration in a requested component for example allowing anexpert user to diagnose the component without physical contact;providing a haptic feedback for a requested component for example if theuser is double checking a lockout/tagout operation before entering acomponent, opening a panel, and/or entering a potentially hazardousarea). The provided examples for operations of the haptic feedbackcircuit 12530 are non-limiting illustrations.

While the foregoing written description enables one skilled in the artto make and use what is considered presently to be the best modethereof, those skilled in the art will understand and appreciate theexistence of variations, combinations, and equivalents of the specificembodiment, method, and examples herein. The disclosure should thereforenot be limited by the above described embodiment, method, and examples,but by all embodiments and methods within the scope and spirit of thedisclosure.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions, and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions, orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationswithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, code,and/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient, and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of a program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code, and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

In embodiments, one or more of the controllers, circuits, systems, datacollectors, storage systems, network elements, components, or the likeas described throughout this disclosure may be embodied in or on anintegrated circuit, such as an analog, digital, or mixed signal circuit,such as a microprocessor, a programmable logic controller, anapplication-specific integrated circuit, a field programmable gatearray, or other circuit, such as embodied on one or more chips disposedon one or more circuit boards, such as to provide in hardware (withpotentially accelerated speed, energy performance, input-outputperformance, or the like) one or more of the functions described herein.This may include setting up circuits with up to billions of logic gates,flip-flops, multiplexers, and other circuits in a small space,facilitating high speed processing, low power dissipation, and reducedmanufacturing cost compared with board-level integration. Inembodiments, a digital IC, typically a microprocessor, digital signalprocessor, microcontroller, or the like may use Boolean algebra toprocess digital signals to embody complex logic, such as involved in thecircuits, controllers, and other systems described herein. Inembodiments, a data collector, an expert system, a storage system, orthe like may be embodied as a digital integrated circuit (“IC”), such asa logic IC, memory chip, interface IC (e.g., a level shifter, aserializer, a deserializer, and the like), a power management IC and/ora programmable device; an analog integrated circuit, such as a linearIC, RF IC, or the like, or a mixed signal IC, such as a data acquisitionIC (including A/D converters, D/A converter, digital potentiometers)and/or a clock/timing IC.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be configured for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g., USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the Figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosure,and does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Implementations of approaches described above may include softwareimplementations, which use software instructions stored onnon-transitory machine-readable media. The procedures and protocols asdescribed above in the text and figures are sufficient for one skilledin the art to implement them in such software implementations. In someexamples, the software may execute on a client node (e.g., a smartphone)using a general-purpose processor that implements a variety of functionson the client node. Software that executes on end nodes or intermediatenetwork nodes may use processors that are dedicated to processingnetwork traffic, for example, being embedded in network processingdevices. In some implementations, certain functions may be implementedin hardware, for example, using Application-Specific Integrated Circuits(ASICs), and/or Field Programmable Gate Arrays (FPGAs), thereby reducingthe load on a general purpose processor.

Note that in some diagrams and figures in this disclosure, networks suchas the internet, carrier networks, internet service provider networks,local area networks (LANs), metro area networks (MANs), wide areanetworks (WANs), storage area networks (SANs), backhaul networks,cellular networks, satellite networks and the like, may be depicted asclouds. Also note, that certain processes may be referred to as takingplace in the cloud and devices may be described as accessing the cloud.In these types of descriptions, the cloud should be understood to besome type of network comprising networking equipment and wireless and/orwired links.

The description above may refer to a client device communicating with aserver, but it should be understood that the technology and techniquesdescribed herein are not limited to those exemplary devices as theend-points of communication connections or sessions. The end-points mayalso be referred to as, or may be, senders, transmitters, transceivers,receivers, servers, video servers, content servers, proxy servers, cloudstorage units, caches, routers, switches, buffers, mobile devices,tablets, smart phones, handsets, computers, set-top boxes, modems,gaming systems, nodes, satellites, base stations, gateways, satelliteground stations, wireless access points, and the like. The devices atany of the end-points or intermediate nodes of communication connectionsor sessions may be commercial media streaming boxes such as thoseimplementing Apple TV, Roku, Chromecast, Amazon Fire, Slingbox, and thelike, or they may be custom media streaming boxes. The devices at theany of the end-points or intermediate nodes of communication connectionsor sessions may be smart televisions and/or displays, smart appliancessuch as hubs, refrigerators, security systems, power panels and thelike, smart vehicles such as cars, boats, busses, trains, planes, carts,and the like, and may be any device on the Internet of Things (IoT). Thedevices at any of the end-points or intermediate nodes of communicationconnections or sessions may be single-board computers and/or purposebuilt computing engines comprising processors such as ARM processors,video processors, system-on-a-chip (SoC), and/or memory such as randomaccess memory (RAM), read only memory (ROM), or any kind of electronicmemory components.

Communication connections or sessions may exist between two routers, twoclients, two network nodes, two servers, two mobile devices, and thelike, or any combination of potential nodes and/or end-point devices. Inmany cases, communication sessions are bi-directional so that bothend-point devices may have the ability to send and receive data. Whilethese variations may not be stated explicitly in every description andexemplary embodiment in this disclosure, it should be understood thatthe technology and techniques we describe herein are intended to beapplied to all types of known end-devices, network nodes and equipmentand transmission links, as well as to future end-devices, network nodesand equipment and transmission links with similar or improvedperformance.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platforms. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and thelike. The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor, or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor, and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions, and programs as described hereinand elsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server, and the like. Theserver may include one or more of memories, processors, computerreadable transitory and/or non-transitory media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other servers, clients, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the server. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client, and the like. The client may include one or more ofmemories, processors, computer readable transitory and/or non-transitorymedia, storage media, ports (physical and virtual), communicationdevices, and interfaces capable of accessing other clients, servers,machines, and devices through a wired or a wireless medium, and thelike. The methods, programs, or codes as described herein and elsewheremay be executed by the client. In addition, other devices required forexecution of methods as described in this application may be consideredas a part of the infrastructure associated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers, andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM, and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (“SaaS”), platformas a service (“PaaS”), and/or infrastructure as a service (“IaaS”).

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (“FDMA”) network or code division multiple access (“CDMA”)network. The cellular network may include mobile devices, cell sites,base stations, repeaters, antennas, towers, and the like. The cellnetwork may be a GSM, GPRS, 3G, EVDO, mesh, or other network types.

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable transitory and/or non-transitorymedia that may include: computer components, devices, and recordingmedia that retain digital data used for computing for some interval oftime; semiconductor storage known as random access memory (“RAM”); massstorage typically for more permanent storage, such as optical discs,forms of magnetic storage like hard disks, tapes, drums, cards and othertypes; processor registers, cache memory, volatile memory, non-volatilememory; optical storage such as CD, DVD; removable media such as flashmemory (e.g. USB sticks or keys), floppy disks, magnetic tape, papertape, punch cards, standalone RAM disks, zip drives, removable massstorage, off-line, and the like; other computer memory such as dynamicmemory, static memory, read/write storage, mutable storage, read only,random access, sequential access, location addressable, fileaddressable, content addressable, network attached storage, storage areanetwork, bar codes, magnetic ink, and the like.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable transitory and/ornon-transitory media having a processor capable of executing programinstructions stored thereon as a monolithic software structure, asstandalone software modules, or as modules that employ externalroutines, code, services, and so forth, or any combination of these, andall such implementations may be within the scope of the presentdisclosure. Examples of such machines may include, but may not belimited to, personal digital assistants, laptops, personal computers,mobile phones, other handheld computing devices, medical equipment,wired or wireless communication devices, transducers, chips,calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers, and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable devices, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

The use of the terms “a,” “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitations of ranges ofvalues herein are merely intended to serve as a shorthand method ofreferring individually to each separate value falling within the range,unless otherwise indicated herein, and each separate value isincorporated into the specification as if it were individually recitedherein. All methods described herein may be performed in any suitableorder unless otherwise indicated herein or otherwise clearlycontradicted by context. The use of any and all examples, or exemplarylanguage (e.g., “such as”) provided herein, is intended merely to betterilluminate the disclosure and does not pose a limitation on the scope ofthe disclosure unless otherwise claimed. No language in thespecification should be construed as indicating any non-claimed elementas essential to the practice of the disclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons of ordinary skill in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present invention the scope of theinvention is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

It is to be understood that the foregoing description is intended toillustrate and not to limit the scope of the invention, some aspects ofwhich are defined by the scope of the appended claims. Furthermore,other embodiments are within the scope of the following claims.

What is claimed is:
 1. A system for data collection in a vehicle, thesystem comprising: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to input received from at least one of a plurality ofinput sensors, each of the plurality of input sensors operativelycoupled to at least one of a plurality of components of the vehicle; adata analysis circuit structured to determine a state value, wherein thedata analysis circuit comprises a pattern recognition circuit structuredto determine the state value by analyzing a subset of the plurality ofdetection values and at least one external detection value using atleast one of a neural net or an expert system; and an analysis responsecircuit structured to adjust a parameter of the vehicle in response tothe state value, wherein the analysis response circuit is furtherstructured to adjust a detection package in response to the state value,wherein the detection package comprises a selection of available sensorsutilized as the plurality of input sensors, wherein the detectionpackage further comprises a sensor parameter for at least one of theplurality of input sensors, and wherein the state value comprises atleast one of: an off-nominal operation, a component failure, a componentfault, or a component maintenance requirement; and wherein the adjustingthe detection package comprises enhancing a resolution of at least oneof the subset of the plurality of detection values in response to thestate value.
 2. The system of claim 1, wherein the enhancing theresolution of the at least one of the subset of the plurality ofdetection values comprises at least one of enhancing a sensorresolution, changing from a first input sensor to a second input sensorhaving a higher resolution capability than the first input sensor, orchanging a data storage profile to enhance a resolution of stored dataof the at least one of the subset of the plurality of detection values.3. The system of claim 1, wherein the at least one of the neural net orthe expert system performs a pattern recognition operation to determinethe state value.
 4. The system of claim 3, wherein the patternrecognition operation is performed on vibration data of the plurality ofdetection values.
 5. The system of claim 4, wherein the at least one ofthe neural net or the expert system further compares the vibration dataof the plurality of detection values to a library of noise patterns,wherein the library of noise patterns comprises the at least oneexternal detection value.
 6. The system of claim 5, wherein the at leastone of the neural net or the expert system is configured to at leastintermittently access a self-organizing marketplace, and wherein theself-organizing marketplace provides the library of noise patterns. 7.The system of claim 6, wherein the at least one of the neural net or theexpert system is configured to provide at least a portion of thevibration data to the self-organizing marketplace.
 8. A method,comprising: interpreting a plurality of detection values of a vehicle,each of the plurality of detection values corresponding to inputreceived from at least one of a plurality of input sensors, each of theplurality of input sensors operatively coupled to at least one componentof the vehicle; operating at least one of a neural net or an expertsystem on the plurality of detection values to determine a state valuefor at least one of a component or the vehicle; and adjusting at leastone of a sensing parameter or a data storage profile in response to thestate value, wherein the state value comprises at least one of: anoff-nominal operation, a component failure, a component fault, or acomponent maintenance requirement; and wherein the adjusting the atleast one of the sensing parameter or the data storage profile comprisesenhancing a resolution of at least one of the plurality of detectionvalues in response to the state value.
 9. The method of claim 8, whereinthe at least one of the neural net or the expert system performs apattern recognition operation to determine the state value.
 10. Themethod of claim 9, wherein the at least one of the neural net or theexpert system accesses at least one external data value from aself-organizing marketplace, and further determines the state value inresponse to the at least one external data value.
 11. The method ofclaim 10, wherein the at least one external data value comprises alibrary of noise patterns.
 12. The method of claim 11, wherein thelibrary of noise patterns comprises a vibration fingerprint for thecomponent of the vehicle.
 13. An apparatus, comprising: a dataacquisition circuit structured to interpret a plurality of detectionvalues, each of the plurality of detection values corresponding to inputreceived from at least one of a plurality of input sensors, each of theplurality of input sensors operatively coupled to at least one of aplurality of components of a vehicle; a data analysis circuit structuredto determine a state value, wherein the data analysis circuit comprisesa pattern recognition circuit structured to determine the state value byperforming a pattern recognition operation on a subset of the pluralityof detection values and at least one external detection value using atleast one of a neural net or an expert system; and an analysis responsecircuit structured to adjust a parameter of the vehicle in response tothe state value, wherein the state value comprises a normal operatingstate for a component of the vehicle, and wherein adjusting theparameter of the vehicle comprises reducing an amount of data of theplurality of detection values that is stored relating to the componentof the vehicle.
 14. The apparatus of claim 13, wherein the adjusting theparameter of the vehicle comprises adjusting operations of the vehicleto reduce a work load on the component of the vehicle.
 15. The apparatusof claim 13, wherein the state value comprises, for the component of thevehicle, at least one of: an off-nominal operation, a failure, a fault,or a maintenance requirement; and wherein the adjusting the parameter ofthe vehicle comprises increasing the amount of data of the plurality ofdetection values that is stored relating to the component of thevehicle.
 16. A system for data collection in a vehicle, the systemcomprising: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to input received from at least one of a plurality ofinput sensors, each of the plurality of input sensors operativelycoupled to at least one of a plurality of components of the vehicle; adata analysis circuit structured to determine a state value, wherein thedata analysis circuit comprises a pattern recognition circuit structuredto determine the state value by analyzing a subset of the plurality ofdetection values and at least one external detection value using atleast one of a neural net or an expert system; and an analysis responsecircuit structured to adjust a parameter of the vehicle in response tothe state value, wherein the at least one of the neural net or theexpert system performs a pattern recognition operation to determine thestate value, wherein the pattern recognition operation is performed onvibration data of the plurality of detection values.
 17. The system ofclaim 16, wherein the analysis response circuit is further structured toadjust a detection package in response to the state value, wherein thedetection package comprises a selection of available sensors utilized asthe plurality of input sensors.
 18. The system of claim 17, wherein thedetection package further comprises a sensor parameter for at least oneof the plurality of input sensors.
 19. The system of claim 18, whereinthe state value comprises at least one of: an off-nominal operation, acomponent failure, a component fault, or a component maintenancerequirement; and wherein the adjusting the detection package comprisesenhancing a resolution of at least one of the plurality of detectionvalues in response to the state value.
 20. The system of claim 19,wherein the enhancing the resolution of the at least one of theplurality of detection values comprises at least one of enhancing asensor resolution, changing from a first input sensor to a second inputsensor having a higher resolution capability than the first inputsensor, or changing a data storage profile to enhance a resolution ofstored data of the subset of the plurality of detection values.
 21. Thesystem of claim 16, wherein the at least one of the neural net or theexpert system further compares the vibration data of the plurality ofdetection values to a library of noise patterns, wherein the library ofnoise patterns comprises the at least one external detection value. 22.The system of claim 21, wherein the at least one of the neural net orthe expert system is configured to at least intermittently access aself-organizing marketplace, and wherein the self-organizing marketplaceprovides the library of noise patterns.
 23. The system of claim 22,wherein the at least one of the neural net or the expert system isconfigured to provide at least a portion of the vibration data to theself-organizing marketplace.
 24. A method, comprising: interpreting aplurality of detection values of a vehicle, each of the plurality ofdetection values corresponding to input received from at least one of aplurality of input sensors, each of the plurality of input sensorsoperatively coupled to at least one component of the vehicle; operatingat least one of a neural net or an expert system on the plurality ofdetection values to determine a state value for the at least one ofcomponent of the vehicle; and adjusting at least one of a sensingparameter or a data storage profile in response to the state value,wherein the at least one of the neural net or the expert system performsa pattern recognition operation to determine the state value, whereinthe state value comprises at least one of: an off-nominal operation, acomponent failure, a component fault, or a component maintenancerequirement, and wherein the adjusting the at least one of the sensingparameter or the data storage profile comprises enhancing a resolutionof at least one of the plurality of detection values in response to thestate value.
 25. The method of claim 24, wherein the at least one of theneural net or the expert system accesses external data value from aself-organizing marketplace, and further determines the state value inresponse to the external data value.
 26. The method of claim 25, whereinthe external data value comprises a library of noise patterns.
 27. Themethod of claim 26, wherein the library of noise patterns comprises avibration fingerprint for the at least one component of the vehicle. 28.An apparatus, comprising: a data acquisition circuit structured tointerpret a plurality of detection values, each of the plurality ofdetection values corresponding to input received from at least one of aplurality of input sensors, each of the plurality of input sensorsoperatively coupled to at least one of a plurality of components of avehicle; a data analysis circuit structured to determine a state value,wherein the data analysis circuit comprises a pattern recognitioncircuit structured to determine the state value by performing a patternrecognition operation on a subset of the plurality of detection valuesand at least one external detection value using at least one of a neuralnet or an expert system; and an analysis response circuit structured toadjust a parameter of the vehicle in response to the state value,wherein the state value comprises, for a component of the vehicle, atleast one of: an off-nominal operation, a failure, a fault, or amaintenance requirement; and wherein the adjusting the parameter of thevehicle comprises increasing an amount of data of the plurality ofdetection values that is stored relating to the component of thevehicle.
 29. The apparatus of claim 28, wherein the adjusting theparameter of the vehicle comprises adjusting operations of the vehicleto reduce a work load on the component of the vehicle.
 30. The apparatusof claim 28, wherein the state value comprises a normal operating statefor the component of the vehicle, and wherein adjusting the parameter ofthe vehicle comprises reducing the amount of data of the plurality ofdetection values that is stored relating to the component of thevehicle.
 31. A system for data collection in a vehicle, the systemcomprising: a data acquisition circuit structured to interpret aplurality of detection values, each of the plurality of detection valuescorresponding to input received from at least one of a plurality ofinput sensors, each of the plurality of input sensors operativelycoupled to at least one of a plurality of components of the vehicle; adata analysis circuit structured to determine a state value, wherein thedata analysis circuit comprises a pattern recognition circuit structuredto determine the state value by analyzing a subset of the plurality ofdetection values and at least one external detection value using atleast one of a neural net or an expert system; and an analysis responsecircuit structured to adjust a parameter of the vehicle in response tothe state value, wherein the analysis response circuit is furtherstructured to adjust a detection package in response to the state value,wherein the detection package comprises a selection of available sensorsutilized as the plurality of input sensors, wherein the at least one ofthe neural net or the expert system performs a pattern recognitionoperation to determine the state value, wherein the pattern recognitionoperation is performed on vibration data of the plurality of detectionvalues, wherein the at least one of the neural net or the expert systemfurther compares the vibration data of the plurality of detection valuesto a library of noise patterns, wherein the library of noise patternscomprises the at least one external detection value, wherein the atleast one of the neural net or the expert system is configured to atleast intermittently access a self-organizing marketplace, and whereinthe self-organizing marketplace provides the library of noise patterns.32. The system of claim 31, wherein the detection package furthercomprises a sensor parameter for at least one of the plurality of inputsensors.
 33. The system of claim 32, wherein the state value comprisesat least one of: an off-nominal operation, a component failure, acomponent fault, or a component maintenance requirement; and wherein theadjusting the detection package comprises enhancing a resolution of atleast one of the subset of the plurality of detection values in responseto the state value.
 34. The system of claim 33, wherein the enhancingthe resolution of the at least one of the subset of the plurality ofdetection values comprises at least one of enhancing a sensorresolution, changing from a first input sensor to a second input sensorhaving a higher resolution capability than the first input sensor, orchanging a data storage profile to enhance a resolution of stored dataof the at least one of the subset of the plurality of detection values.35. The system of claim 31, wherein the at least one of the neural netor the expert system is configured to provide at least a portion of thevibration data to the self-organizing marketplace.
 36. A method,comprising: interpreting a plurality of detection values of a vehicle,each of the plurality of detection values corresponding to inputreceived from at least one of a plurality of input sensors, each of theplurality of input sensors operatively coupled to at least one componentof the vehicle; operating at least one of a neural net or an expertsystem on the plurality of detection values to determine a state valuefor the at least one of component of the vehicle; and adjusting at leastone of a sensing parameter or a data storage profile in response to thestate value, wherein the at least one of the neural net or the expertsystem performs a pattern recognition operation to determine the statevalue, and wherein the at least one of the neural net or the expertsystem accesses external data value from a self-organizing marketplace,and further determines the state value in response to the external datavalue.
 37. The method of claim 36, wherein the state value comprises atleast one of: an off-nominal operation, a component failure, a componentfault, or a component maintenance requirement; and wherein the adjustingthe at least one of the sensing parameter or the data storage profilecomprises enhancing a resolution of at least one of the plurality ofdetection values in response to the state value.
 38. The method of claim36, wherein the external data value comprises a library of noisepatterns.
 39. The method of claim 38, wherein the library of noisepatterns comprises a vibration fingerprint for the at least onecomponent of the vehicle.